diff --git a/tutorials/README.md b/tutorials/README.md
index fa0a9a9d..6e9c3023 100644
--- a/tutorials/README.md
+++ b/tutorials/README.md
@@ -17,26 +17,26 @@ Use this guide to navigate all tutorial tracks, understand structure rules, and
 <<<<<<< HEAD
 | Tutorial directories | 191 |
 | Tutorial markdown files | 1732 |
-| Tutorial markdown lines | 1,004,205 |
+| Tutorial markdown lines | 1,048,791 |
 =======
 <<<<<<< HEAD
 | Tutorial directories | 191 |
 | Tutorial markdown files | 1732 |
-| Tutorial markdown lines | 1,004,205 |
+| Tutorial markdown lines | 1,048,791 |
 =======
 <<<<<<< HEAD
 | Tutorial directories | 191 |
 | Tutorial markdown files | 1732 |
-| Tutorial markdown lines | 1,004,205 |
+| Tutorial markdown lines | 1,048,791 |
 =======
 <<<<<<< HEAD
 | Tutorial directories | 191 |
 | Tutorial markdown files | 1732 |
-| Tutorial markdown lines | 1,004,205 |
+| Tutorial markdown lines | 1,048,791 |
 =======
 | Tutorial directories | 191 |
 | Tutorial markdown files | 1732 |
-| Tutorial markdown lines | 1,004,205 |
+| Tutorial markdown lines | 1,048,791 |
 
 ## Source Verification Snapshot
 
diff --git a/tutorials/anthropic-skills-tutorial/01-getting-started.md b/tutorials/anthropic-skills-tutorial/01-getting-started.md
index bdba7a2a..98ef442a 100644
--- a/tutorials/anthropic-skills-tutorial/01-getting-started.md
+++ b/tutorials/anthropic-skills-tutorial/01-getting-started.md
@@ -142,3 +142,449 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 2: Skill Categories](02-skill-categories.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- tutorial slug: **anthropic-skills-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Anthropic Skills 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
+
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/anthropic-skills-tutorial/02-skill-categories.md b/tutorials/anthropic-skills-tutorial/02-skill-categories.md
index 29faf82f..51c039a3 100644
--- a/tutorials/anthropic-skills-tutorial/02-skill-categories.md
+++ b/tutorials/anthropic-skills-tutorial/02-skill-categories.md
@@ -109,3 +109,473 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 3: Advanced Skill Design](03-advanced-skill-design.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- tutorial slug: **anthropic-skills-tutorial**
+- chapter focus: **Chapter 2: Skill Categories**
+- system context: **Anthropic Skills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Skill Categories`.
+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
+
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Skill Categories`.
+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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Categories
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/anthropic-skills-tutorial/03-advanced-skill-design.md b/tutorials/anthropic-skills-tutorial/03-advanced-skill-design.md
index 0931168f..68593420 100644
--- a/tutorials/anthropic-skills-tutorial/03-advanced-skill-design.md
+++ b/tutorials/anthropic-skills-tutorial/03-advanced-skill-design.md
@@ -134,3 +134,449 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 4: Integration Platforms](04-integration-platforms.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- tutorial slug: **anthropic-skills-tutorial**
+- chapter focus: **Chapter 3: Advanced Skill Design**
+- system context: **Anthropic Skills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Advanced Skill 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
+
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Advanced Skill 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 3: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Skill Design
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/anthropic-skills-tutorial/04-integration-platforms.md b/tutorials/anthropic-skills-tutorial/04-integration-platforms.md
index 6658fbaa..a51ee0b1 100644
--- a/tutorials/anthropic-skills-tutorial/04-integration-platforms.md
+++ b/tutorials/anthropic-skills-tutorial/04-integration-platforms.md
@@ -124,3 +124,461 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 5: Production Skills](05-production-skills.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- tutorial slug: **anthropic-skills-tutorial**
+- chapter focus: **Chapter 4: Integration Platforms**
+- system context: **Anthropic Skills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Integration Platforms`.
+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
+
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Integration Platforms`.
+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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Integration Platforms
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/anthropic-skills-tutorial/05-production-skills.md b/tutorials/anthropic-skills-tutorial/05-production-skills.md
index 1b2ef46d..e6f13b3a 100644
--- a/tutorials/anthropic-skills-tutorial/05-production-skills.md
+++ b/tutorials/anthropic-skills-tutorial/05-production-skills.md
@@ -124,3 +124,461 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 6: Best Practices](06-best-practices.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- tutorial slug: **anthropic-skills-tutorial**
+- chapter focus: **Chapter 5: Production Skills**
+- system context: **Anthropic Skills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Production Skills`.
+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
+
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Production Skills`.
+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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Production Skills
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/anthropic-skills-tutorial/06-best-practices.md b/tutorials/anthropic-skills-tutorial/06-best-practices.md
index 11641110..dcbde1b4 100644
--- a/tutorials/anthropic-skills-tutorial/06-best-practices.md
+++ b/tutorials/anthropic-skills-tutorial/06-best-practices.md
@@ -109,3 +109,473 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 7: Publishing and Sharing](07-publishing-sharing.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- tutorial slug: **anthropic-skills-tutorial**
+- chapter focus: **Chapter 6: Best Practices**
+- system context: **Anthropic Skills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: 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
+
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: 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 6: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Best Practices
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/anthropic-skills-tutorial/07-publishing-sharing.md b/tutorials/anthropic-skills-tutorial/07-publishing-sharing.md
index 70e8526f..8943b236 100644
--- a/tutorials/anthropic-skills-tutorial/07-publishing-sharing.md
+++ b/tutorials/anthropic-skills-tutorial/07-publishing-sharing.md
@@ -109,3 +109,473 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 8: Real-World Examples](08-real-world-examples.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- tutorial slug: **anthropic-skills-tutorial**
+- chapter focus: **Chapter 7: Publishing and Sharing**
+- system context: **Anthropic Skills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Publishing and Sharing`.
+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
+
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Publishing and Sharing`.
+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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Publishing and Sharing
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/anthropic-skills-tutorial/08-real-world-examples.md b/tutorials/anthropic-skills-tutorial/08-real-world-examples.md
index 3b115e55..072b419d 100644
--- a/tutorials/anthropic-skills-tutorial/08-real-world-examples.md
+++ b/tutorials/anthropic-skills-tutorial/08-real-world-examples.md
@@ -135,3 +135,449 @@ Suggested trace strategy:
 - [Previous Chapter: Chapter 7: Publishing and Sharing](07-publishing-sharing.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- tutorial slug: **anthropic-skills-tutorial**
+- chapter focus: **Chapter 8: Real-World Examples**
+- system context: **Anthropic Skills Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Real-World Examples`.
+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
+
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Servers Tutorial](../mcp-servers-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [anthropics/skills repository](https://github.com/anthropics/skills)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Real-World Examples`.
+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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Real-World Examples
+
+- tutorial context: **Anthropic Skills Tutorial: Reusable AI Agent Capabilities**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/anthropic-skills-tutorial/index.md b/tutorials/anthropic-skills-tutorial/index.md
index e77b955d..c3da8da3 100644
--- a/tutorials/anthropic-skills-tutorial/index.md
+++ b/tutorials/anthropic-skills-tutorial/index.md
@@ -3,6 +3,7 @@ layout: default
 title: "Anthropic Skills Tutorial"
 nav_order: 91
 has_children: true
+format_version: v2
 ---
 
 # Anthropic Skills Tutorial: Reusable AI Agent Capabilities
@@ -13,6 +14,16 @@ has_children: true
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Spec](https://img.shields.io/badge/Spec-agentskills.io-blue)](https://agentskills.io/specification)
 
+## Why This Track Matters
+
+Anthropic Skills let you package reusable, reliable behaviors for Claude agents once and deploy them across every integration point — Claude Code, Claude.ai, and the API — without re-engineering each time.
+
+This track focuses on:
+- designing skills with clear invocation boundaries and deterministic outputs
+- packaging repeatable workflows using scripts, references, and asset files
+- publishing versioned skills for team or public reuse
+- operating a skills catalog with ownership and lifecycle controls
+
 ## What are Anthropic Skills?
 
 Anthropic Skills are packaged instructions and supporting files that Claude can load for specific jobs. A skill can be lightweight (one `SKILL.md`) or operationally rich (scripts, templates, and domain references).
@@ -35,7 +46,7 @@ The official `anthropics/skills` repository demonstrates real patterns used for:
 | `references/` | Source material Claude can load on demand for better answers |
 | `assets/` | Non-text files required by the workflow |
 
-## Tutorial Structure
+## Chapter Guide
 
 | Chapter | Topic | What You Will Learn |
 |:--------|:------|:--------------------|
@@ -110,11 +121,24 @@ Ready to begin? Start with [Chapter 1: Getting Started](01-getting-started.md).
 7. [Chapter 7: Publishing and Sharing](07-publishing-sharing.md)
 8. [Chapter 8: Real-World Examples](08-real-world-examples.md)
 
+## Current Snapshot (auto-updated)
+
+- repository: [anthropics/skills](https://github.com/anthropics/skills)
+- stars: about **1.2K**
+- project positioning: official reference implementation for the Agent Skills format specification
+
+## What You Will Learn
+
+- how to design and structure a SKILL.md file with frontmatter and behavioral contracts
+- how to compose multi-file skills with scripts, references, and asset directories
+- how to integrate skills across Claude Code, Claude.ai, and the Claude API
+- how to version, publish, and maintain skills catalogs for team-wide reuse
+
 ## Source References
 
 - [anthropics/skills repository](https://github.com/anthropics/skills)
 
-## Concept Flow
+## Mental Model
 
 ```mermaid
 flowchart TD
diff --git a/tutorials/athens-research-knowledge-graph/01-system-overview.md b/tutorials/athens-research-knowledge-graph/01-system-overview.md
index 70668664..5ba10692 100644
--- a/tutorials/athens-research-knowledge-graph/01-system-overview.md
+++ b/tutorials/athens-research-knowledge-graph/01-system-overview.md
@@ -493,3 +493,97 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 2: Datascript Deep Dive](02-datascript-database.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Athens Research: Deep Dive Tutorial**
+- tutorial slug: **athens-research-knowledge-graph**
+- chapter focus: **Chapter 1: System Overview**
+- system context: **Athens Research Knowledge Graph**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: System Overview`.
+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
+
+- [Athens Research](https://github.com/athensresearch/athens)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: System Overview`.
+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?
diff --git a/tutorials/athens-research-knowledge-graph/04-app-architecture.md b/tutorials/athens-research-knowledge-graph/04-app-architecture.md
index bdd74d83..4676dc58 100644
--- a/tutorials/athens-research-knowledge-graph/04-app-architecture.md
+++ b/tutorials/athens-research-knowledge-graph/04-app-architecture.md
@@ -101,3 +101,481 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 5: Component System](05-component-system.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Athens Research: Deep Dive Tutorial**
+- tutorial slug: **athens-research-knowledge-graph**
+- chapter focus: **Chapter 4: Application Architecture**
+- system context: **Athens Research Knowledge Graph**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Application 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
+
+- [Athens Research](https://github.com/athensresearch/athens)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Application 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 4: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Application Architecture
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/athens-research-knowledge-graph/05-component-system.md b/tutorials/athens-research-knowledge-graph/05-component-system.md
index 007ac399..22ddf576 100644
--- a/tutorials/athens-research-knowledge-graph/05-component-system.md
+++ b/tutorials/athens-research-knowledge-graph/05-component-system.md
@@ -94,3 +94,493 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 6: Event Handling](06-event-handling.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Athens Research: Deep Dive Tutorial**
+- tutorial slug: **athens-research-knowledge-graph**
+- chapter focus: **Chapter 5: Component System**
+- system context: **Athens Research Knowledge Graph**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Component System`.
+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
+
+- [Athens Research](https://github.com/athensresearch/athens)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Component System`.
+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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Component System
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/athens-research-knowledge-graph/06-event-handling.md b/tutorials/athens-research-knowledge-graph/06-event-handling.md
index 11e505e6..692b3c89 100644
--- a/tutorials/athens-research-knowledge-graph/06-event-handling.md
+++ b/tutorials/athens-research-knowledge-graph/06-event-handling.md
@@ -86,3 +86,505 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 7: Block Editor](07-block-editor.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Athens Research: Deep Dive Tutorial**
+- tutorial slug: **athens-research-knowledge-graph**
+- chapter focus: **Chapter 6: Event Handling**
+- system context: **Athens Research Knowledge Graph**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Event Handling`.
+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
+
+- [Athens Research](https://github.com/athensresearch/athens)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Event Handling`.
+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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Event Handling
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/athens-research-knowledge-graph/07-block-editor.md b/tutorials/athens-research-knowledge-graph/07-block-editor.md
index 15fca8f8..251c3f41 100644
--- a/tutorials/athens-research-knowledge-graph/07-block-editor.md
+++ b/tutorials/athens-research-knowledge-graph/07-block-editor.md
@@ -91,3 +91,493 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 8: Rich Text](08-rich-text.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Athens Research: Deep Dive Tutorial**
+- tutorial slug: **athens-research-knowledge-graph**
+- chapter focus: **Chapter 7: Block Editor**
+- system context: **Athens Research Knowledge Graph**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Block Editor`.
+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
+
+- [Athens Research](https://github.com/athensresearch/athens)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Block Editor`.
+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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/athens-research-knowledge-graph/08-rich-text.md b/tutorials/athens-research-knowledge-graph/08-rich-text.md
index 55d99d9d..0be9f446 100644
--- a/tutorials/athens-research-knowledge-graph/08-rich-text.md
+++ b/tutorials/athens-research-knowledge-graph/08-rich-text.md
@@ -84,3 +84,505 @@ Suggested trace strategy:
 - [Previous Chapter: Chapter 7: Block Editor](07-block-editor.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Athens Research: Deep Dive Tutorial**
+- tutorial slug: **athens-research-knowledge-graph**
+- chapter focus: **Chapter 8: Rich Text**
+- system context: **Athens Research Knowledge Graph**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Rich Text`.
+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
+
+- [Athens Research](https://github.com/athensresearch/athens)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Rich Text`.
+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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Rich Text
+
+- tutorial context: **Athens Research: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/athens-research-knowledge-graph/index.md b/tutorials/athens-research-knowledge-graph/index.md
index fcd9c9c2..22970940 100644
--- a/tutorials/athens-research-knowledge-graph/index.md
+++ b/tutorials/athens-research-knowledge-graph/index.md
@@ -3,6 +3,7 @@ layout: default
 title: "Athens Research Knowledge Graph"
 nav_order: 39
 has_children: true
+format_version: v2
 ---
 
 # Athens Research: Deep Dive Tutorial
@@ -13,6 +14,16 @@ has_children: true
 [![License: EPL 1.0](https://img.shields.io/badge/License-EPL_1.0-blue.svg)](https://www.eclipse.org/legal/epl-v10.html)
 [![ClojureScript](https://img.shields.io/badge/ClojureScript-Reagent-purple)](https://github.com/athensresearch/athens)
 
+## Why This Track Matters
+
+Athens Research demonstrates how a graph-first, local-first knowledge system can be built with ClojureScript and Datascript, offering a fully self-hosted alternative to cloud knowledge tools.
+
+This track focuses on:
+- understanding block-based editing with bi-directional link management
+- working with Datascript in-memory graph databases for knowledge relationships
+- building ClojureScript frontends with Re-frame state management
+- operating a local-first system with optional real-time collaboration
+
 ## What Is Athens Research?
 
 Athens is an open-source knowledge management system inspired by Roam Research. It uses Datascript (an in-memory graph database) with ClojureScript to provide block-based editing, bi-directional linking, and knowledge graph visualization — all running locally for full data ownership.
@@ -26,7 +37,7 @@ Athens is an open-source knowledge management system inspired by Roam Research.
 | **Local-First** | All data stored locally, no cloud dependency |
 | **Real-Time Collab** | Multi-user editing with conflict resolution |
 
-## Architecture Overview
+## Mental Model
 
 ```mermaid
 graph TB
@@ -51,7 +62,7 @@ graph TB
     State --> Data
 ```
 
-## Tutorial Structure
+## Chapter Guide
 
 | Chapter | Topic | What You'll Learn |
 |---------|-------|-------------------|
@@ -101,6 +112,19 @@ Ready to begin? Start with [Chapter 1: System Overview](01-system-overview.md).
 7. [Chapter 7: Block Editor](07-block-editor.md)
 8. [Chapter 8: Rich Text](08-rich-text.md)
 
+## Current Snapshot (auto-updated)
+
+- repository: [athensresearch/athens](https://github.com/athensresearch/athens)
+- stars: about **9.5K**
+- project positioning: open-source Roam Research alternative with graph database architecture
+
+## What You Will Learn
+
+- how Athens uses Datascript as an in-memory graph database for knowledge storage
+- how bi-directional links and backlinks are managed across pages and blocks
+- how Re-frame events and subscriptions drive the ClojureScript application state
+- how the block editor handles recursive rendering and outliner-style editing
+
 ## Source References
 
 - [Athens Research](https://github.com/athensresearch/athens)
diff --git a/tutorials/babyagi-tutorial/01-getting-started.md b/tutorials/babyagi-tutorial/01-getting-started.md
index cc5c31d4..3ea80814 100644
--- a/tutorials/babyagi-tutorial/01-getting-started.md
+++ b/tutorials/babyagi-tutorial/01-getting-started.md
@@ -280,3 +280,303 @@ Use the following upstream sources to verify implementation details while readin
 - [Next Chapter: Chapter 2: Core Architecture: Task Queue and Agent Loop](02-core-architecture-task-queue-and-agent-loop.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/babyagi-tutorial/02-core-architecture-task-queue-and-agent-loop.md b/tutorials/babyagi-tutorial/02-core-architecture-task-queue-and-agent-loop.md
index c5c42b9e..f8ff12a0 100644
--- a/tutorials/babyagi-tutorial/02-core-architecture-task-queue-and-agent-loop.md
+++ b/tutorials/babyagi-tutorial/02-core-architecture-task-queue-and-agent-loop.md
@@ -296,3 +296,291 @@ Use the following upstream sources to verify implementation details while readin
 - [Next Chapter: Chapter 3: LLM Backend Integration and Configuration](03-llm-backend-integration-and-configuration.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 2: Core Architecture: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Task Queue and Agent Loop
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/babyagi-tutorial/03-llm-backend-integration-and-configuration.md b/tutorials/babyagi-tutorial/03-llm-backend-integration-and-configuration.md
index 2a171817..ab0483b7 100644
--- a/tutorials/babyagi-tutorial/03-llm-backend-integration-and-configuration.md
+++ b/tutorials/babyagi-tutorial/03-llm-backend-integration-and-configuration.md
@@ -307,3 +307,279 @@ Use the following upstream sources to verify implementation details while readin
 - [Next Chapter: Chapter 4: Task Creation and Prioritization Engine](04-task-creation-and-prioritization-engine.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 3: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: LLM Backend Integration and Configuration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/babyagi-tutorial/04-task-creation-and-prioritization-engine.md b/tutorials/babyagi-tutorial/04-task-creation-and-prioritization-engine.md
index 9a9ce452..55e5993c 100644
--- a/tutorials/babyagi-tutorial/04-task-creation-and-prioritization-engine.md
+++ b/tutorials/babyagi-tutorial/04-task-creation-and-prioritization-engine.md
@@ -313,3 +313,279 @@ Use the following upstream sources to verify implementation details while readin
 - [Next Chapter: Chapter 5: Memory Systems and Vector Store Integration](05-memory-systems-and-vector-store-integration.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 4: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Task Creation and Prioritization Engine
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/babyagi-tutorial/05-memory-systems-and-vector-store-integration.md b/tutorials/babyagi-tutorial/05-memory-systems-and-vector-store-integration.md
index 2e837f4f..f03b694d 100644
--- a/tutorials/babyagi-tutorial/05-memory-systems-and-vector-store-integration.md
+++ b/tutorials/babyagi-tutorial/05-memory-systems-and-vector-store-integration.md
@@ -311,3 +311,279 @@ Use the following upstream sources to verify implementation details while readin
 - [Next Chapter: Chapter 6: Extending BabyAGI: Custom Tools and Skills](06-extending-babyagi-custom-tools-and-skills.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 5: Memory Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Systems and Vector Store Integration
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/babyagi-tutorial/06-extending-babyagi-custom-tools-and-skills.md b/tutorials/babyagi-tutorial/06-extending-babyagi-custom-tools-and-skills.md
index a7cbcf06..6988c0e4 100644
--- a/tutorials/babyagi-tutorial/06-extending-babyagi-custom-tools-and-skills.md
+++ b/tutorials/babyagi-tutorial/06-extending-babyagi-custom-tools-and-skills.md
@@ -326,3 +326,255 @@ Use the following upstream sources to verify implementation details while readin
 - [Next Chapter: Chapter 7: BabyAGI Evolution: 2o and Functionz Framework](07-babyagi-evolution-2o-and-functionz-framework.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 6: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Extending BabyAGI: Custom Tools and Skills
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/babyagi-tutorial/07-babyagi-evolution-2o-and-functionz-framework.md b/tutorials/babyagi-tutorial/07-babyagi-evolution-2o-and-functionz-framework.md
index 2574674a..6d19d3e4 100644
--- a/tutorials/babyagi-tutorial/07-babyagi-evolution-2o-and-functionz-framework.md
+++ b/tutorials/babyagi-tutorial/07-babyagi-evolution-2o-and-functionz-framework.md
@@ -327,3 +327,255 @@ Use the following upstream sources to verify implementation details while readin
 - [Next Chapter: Chapter 8: Production Patterns and Research Adaptations](08-production-patterns-and-research-adaptations.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 7: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: BabyAGI Evolution: 2o and Functionz Framework
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/babyagi-tutorial/08-production-patterns-and-research-adaptations.md b/tutorials/babyagi-tutorial/08-production-patterns-and-research-adaptations.md
index 9ab991a8..fd54ad47 100644
--- a/tutorials/babyagi-tutorial/08-production-patterns-and-research-adaptations.md
+++ b/tutorials/babyagi-tutorial/08-production-patterns-and-research-adaptations.md
@@ -352,3 +352,231 @@ Use the following upstream sources to verify implementation details while readin
 - [Previous Chapter: Chapter 7: BabyAGI Evolution: 2o and Functionz Framework](07-babyagi-evolution-2o-and-functionz-framework.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 8: Production Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Patterns and Research Adaptations
+
+- tutorial context: **BabyAGI Tutorial: The Original Autonomous AI Task Agent Framework**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/claude-quickstarts-tutorial/01-getting-started.md b/tutorials/claude-quickstarts-tutorial/01-getting-started.md
index 8e46e41e..715e91ff 100644
--- a/tutorials/claude-quickstarts-tutorial/01-getting-started.md
+++ b/tutorials/claude-quickstarts-tutorial/01-getting-started.md
@@ -89,3 +89,496 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 2: Customer Support Agents](02-customer-support-agents.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- tutorial slug: **claude-quickstarts-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Claude Quickstarts 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
+
+- [Claude Quickstarts repository](https://github.com/anthropics/anthropic-quickstarts)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+
+### 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/claude-quickstarts-tutorial/02-customer-support-agents.md b/tutorials/claude-quickstarts-tutorial/02-customer-support-agents.md
index e60b1a6e..b9227432 100644
--- a/tutorials/claude-quickstarts-tutorial/02-customer-support-agents.md
+++ b/tutorials/claude-quickstarts-tutorial/02-customer-support-agents.md
@@ -89,3 +89,496 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 3: Data Processing and Analysis](03-data-processing-analysis.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- tutorial slug: **claude-quickstarts-tutorial**
+- chapter focus: **Chapter 2: Customer Support Agents**
+- system context: **Claude Quickstarts Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Customer Support 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
+
+- [Claude Quickstarts repository](https://github.com/anthropics/anthropic-quickstarts)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Customer Support 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 2: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Customer Support Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/claude-quickstarts-tutorial/03-data-processing-analysis.md b/tutorials/claude-quickstarts-tutorial/03-data-processing-analysis.md
index f9381ca6..188116b8 100644
--- a/tutorials/claude-quickstarts-tutorial/03-data-processing-analysis.md
+++ b/tutorials/claude-quickstarts-tutorial/03-data-processing-analysis.md
@@ -86,3 +86,496 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 4: Browser and Computer Use](04-browser-computer-use.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- tutorial slug: **claude-quickstarts-tutorial**
+- chapter focus: **Chapter 3: Data Processing and Analysis**
+- system context: **Claude Quickstarts Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Data Processing and Analysis`.
+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
+
+- [Claude Quickstarts repository](https://github.com/anthropics/anthropic-quickstarts)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Data Processing and Analysis`.
+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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Data Processing and Analysis
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/claude-quickstarts-tutorial/04-browser-computer-use.md b/tutorials/claude-quickstarts-tutorial/04-browser-computer-use.md
index 64df6ba8..712d1d8f 100644
--- a/tutorials/claude-quickstarts-tutorial/04-browser-computer-use.md
+++ b/tutorials/claude-quickstarts-tutorial/04-browser-computer-use.md
@@ -111,3 +111,472 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 5: Autonomous Coding Agents](05-autonomous-coding-agents.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- tutorial slug: **claude-quickstarts-tutorial**
+- chapter focus: **Chapter 4: Browser and Computer Use**
+- system context: **Claude Quickstarts Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Browser and Computer Use`.
+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
+
+- [Claude Quickstarts repository](https://github.com/anthropics/anthropic-quickstarts)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Browser and Computer Use`.
+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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Browser and Computer Use
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/claude-quickstarts-tutorial/05-autonomous-coding-agents.md b/tutorials/claude-quickstarts-tutorial/05-autonomous-coding-agents.md
index ccfb0bea..238d7b12 100644
--- a/tutorials/claude-quickstarts-tutorial/05-autonomous-coding-agents.md
+++ b/tutorials/claude-quickstarts-tutorial/05-autonomous-coding-agents.md
@@ -109,3 +109,472 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 6: Production Patterns](06-production-patterns.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- tutorial slug: **claude-quickstarts-tutorial**
+- chapter focus: **Chapter 5: Autonomous Coding Agents**
+- system context: **Claude Quickstarts Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Autonomous Coding 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
+
+- [Claude Quickstarts repository](https://github.com/anthropics/anthropic-quickstarts)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Autonomous Coding 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 5: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Coding Agents
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/claude-quickstarts-tutorial/06-production-patterns.md b/tutorials/claude-quickstarts-tutorial/06-production-patterns.md
index 10297300..a5b780f8 100644
--- a/tutorials/claude-quickstarts-tutorial/06-production-patterns.md
+++ b/tutorials/claude-quickstarts-tutorial/06-production-patterns.md
@@ -85,3 +85,496 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 7: Evaluation and Guardrails](07-evaluation-guardrails.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- tutorial slug: **claude-quickstarts-tutorial**
+- chapter focus: **Chapter 6: Production Patterns**
+- system context: **Claude Quickstarts Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Production 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
+
+- [Claude Quickstarts repository](https://github.com/anthropics/anthropic-quickstarts)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Production 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Production Patterns
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/claude-quickstarts-tutorial/07-evaluation-guardrails.md b/tutorials/claude-quickstarts-tutorial/07-evaluation-guardrails.md
index 58e737d2..ec002b86 100644
--- a/tutorials/claude-quickstarts-tutorial/07-evaluation-guardrails.md
+++ b/tutorials/claude-quickstarts-tutorial/07-evaluation-guardrails.md
@@ -81,3 +81,508 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 8: Enterprise Operations](08-enterprise-operations.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- tutorial slug: **claude-quickstarts-tutorial**
+- chapter focus: **Chapter 7: Evaluation and Guardrails**
+- system context: **Claude Quickstarts Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Evaluation and Guardrails`.
+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
+
+- [Claude Quickstarts repository](https://github.com/anthropics/anthropic-quickstarts)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Evaluation and Guardrails`.
+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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Evaluation and Guardrails
+
+- tutorial context: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/claude-quickstarts-tutorial/08-enterprise-operations.md b/tutorials/claude-quickstarts-tutorial/08-enterprise-operations.md
index da57b4a9..51e9e02f 100644
--- a/tutorials/claude-quickstarts-tutorial/08-enterprise-operations.md
+++ b/tutorials/claude-quickstarts-tutorial/08-enterprise-operations.md
@@ -106,3 +106,484 @@ Suggested trace strategy:
 - [Previous Chapter: Chapter 7: Evaluation and Guardrails](07-evaluation-guardrails.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- tutorial slug: **claude-quickstarts-tutorial**
+- chapter focus: **Chapter 8: Enterprise Operations**
+- system context: **Claude Quickstarts 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
+
+- [Claude Quickstarts repository](https://github.com/anthropics/anthropic-quickstarts)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [Anthropic API Tutorial](../anthropic-code-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [Claude Code Tutorial](../claude-code-tutorial/)
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+
+### 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Claude Quickstarts Tutorial: Production Integration Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/claude-quickstarts-tutorial/index.md b/tutorials/claude-quickstarts-tutorial/index.md
index 1caae281..bb50187b 100644
--- a/tutorials/claude-quickstarts-tutorial/index.md
+++ b/tutorials/claude-quickstarts-tutorial/index.md
@@ -3,6 +3,7 @@ layout: default
 title: "Claude Quickstarts Tutorial"
 nav_order: 96
 has_children: true
+format_version: v2
 ---
 
 # Claude Quickstarts Tutorial: Production Integration Patterns
@@ -13,6 +14,16 @@ has_children: true
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Languages](https://img.shields.io/badge/Python-TypeScript-blue)](https://github.com/anthropics/anthropic-quickstarts)
 
+## Why This Track Matters
+
+Anthropic's official quickstart projects are the fastest path from API key to production-quality Claude integration, covering the full spectrum from support chatbots to autonomous coding agents.
+
+This track focuses on:
+- building deployable applications using Anthropic's reference architectures
+- applying best practices for error handling, monitoring, and security
+- implementing tool use and multi-agent patterns from working examples
+- deploying Claude-powered applications with Docker and cloud platforms
+
 ## 🎯 What are Claude Quickstarts?
 
 **Claude Quickstarts** is Anthropic's official collection of reference projects demonstrating production-ready patterns for building with Claude. Each quickstart is a complete, deployable application showcasing best practices for specific use cases from customer support to autonomous coding agents.
@@ -28,7 +39,7 @@ has_children: true
 | **Claude Agent SDK** | Demonstrates multi-agent patterns and tool use |
 | **Deployment Guides** | Docker, cloud platforms, scaling strategies |
 
-## Architecture Overview
+## Mental Model
 
 ```mermaid
 graph TB
@@ -75,7 +86,7 @@ graph TB
     class KB,VIZ,DESKTOP,WEB,CODE feature
 ```
 
-## Tutorial Structure
+## Chapter Guide
 
 | Chapter | Topic | What You'll Learn |
 |:--------|:------|:------------------|
@@ -251,6 +262,19 @@ Ready to begin? Start with [Chapter 1: Getting Started](01-getting-started.md).
 7. [Chapter 7: Evaluation and Guardrails](07-evaluation-guardrails.md)
 8. [Chapter 8: Enterprise Operations](08-enterprise-operations.md)
 
+## Current Snapshot (auto-updated)
+
+- repository: [anthropics/anthropic-quickstarts](https://github.com/anthropics/anthropic-quickstarts)
+- stars: about **7.5K**
+- project positioning: official Anthropic reference projects for production Claude integrations
+
+## What You Will Learn
+
+- how to build production-ready Claude applications from Anthropic's reference architectures
+- how to implement tool use, multi-agent patterns, and browser automation with Claude
+- how to handle errors, monitor performance, and apply security best practices
+- how to deploy Claude applications with Docker and scale them for production traffic
+
 ## Source References
 
 - [Claude Quickstarts repository](https://github.com/anthropics/anthropic-quickstarts)
diff --git a/tutorials/devika-tutorial/01-getting-started.md b/tutorials/devika-tutorial/01-getting-started.md
index dceb7abc..df16e41a 100644
--- a/tutorials/devika-tutorial/01-getting-started.md
+++ b/tutorials/devika-tutorial/01-getting-started.md
@@ -225,3 +225,363 @@ Devika's installation complexity stems from having three distinct runtimes (Pyth
 - [Next Chapter: Chapter 2: Architecture and Agent Pipeline](02-architecture-and-agent-pipeline.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/devika-tutorial/02-architecture-and-agent-pipeline.md b/tutorials/devika-tutorial/02-architecture-and-agent-pipeline.md
index 927e3e59..1821b224 100644
--- a/tutorials/devika-tutorial/02-architecture-and-agent-pipeline.md
+++ b/tutorials/devika-tutorial/02-architecture-and-agent-pipeline.md
@@ -226,3 +226,363 @@ Devika's multi-agent architecture solves the single-agent context window and cap
 - [Next Chapter: Chapter 3: LLM Provider Configuration](03-llm-provider-configuration.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 2: Architecture and Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Agent Pipeline
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/devika-tutorial/03-llm-provider-configuration.md b/tutorials/devika-tutorial/03-llm-provider-configuration.md
index 1da67db7..616e6c47 100644
--- a/tutorials/devika-tutorial/03-llm-provider-configuration.md
+++ b/tutorials/devika-tutorial/03-llm-provider-configuration.md
@@ -226,3 +226,363 @@ Devika's multi-provider configuration model solves the vendor lock-in and cost o
 - [Next Chapter: Chapter 4: Task Planning and Code Generation](04-task-planning-and-code-generation.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 3: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: LLM Provider Configuration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/devika-tutorial/04-task-planning-and-code-generation.md b/tutorials/devika-tutorial/04-task-planning-and-code-generation.md
index 8e42d7bc..51de5d69 100644
--- a/tutorials/devika-tutorial/04-task-planning-and-code-generation.md
+++ b/tutorials/devika-tutorial/04-task-planning-and-code-generation.md
@@ -226,3 +226,363 @@ Devika's task planning and code generation pipeline solves the coherence problem
 - [Next Chapter: Chapter 5: Web Research and Browser Integration](05-web-research-and-browser-integration.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 4: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Task Planning and Code Generation
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/devika-tutorial/05-web-research-and-browser-integration.md b/tutorials/devika-tutorial/05-web-research-and-browser-integration.md
index 08c731ee..60d2bea8 100644
--- a/tutorials/devika-tutorial/05-web-research-and-browser-integration.md
+++ b/tutorials/devika-tutorial/05-web-research-and-browser-integration.md
@@ -226,3 +226,363 @@ Devika's browser research integration solves the knowledge cutoff and documentat
 - [Next Chapter: Chapter 6: Project Management and Workspaces](06-project-management-and-workspaces.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 5: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Web Research and Browser Integration
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/devika-tutorial/06-project-management-and-workspaces.md b/tutorials/devika-tutorial/06-project-management-and-workspaces.md
index ddceaffd..9230dee7 100644
--- a/tutorials/devika-tutorial/06-project-management-and-workspaces.md
+++ b/tutorials/devika-tutorial/06-project-management-and-workspaces.md
@@ -226,3 +226,363 @@ Devika's project and workspace management layer solves the isolation and traceab
 - [Next Chapter: Chapter 7: Debugging and Troubleshooting](07-debugging-and-troubleshooting.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 6: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Project Management and Workspaces
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/devika-tutorial/07-debugging-and-troubleshooting.md b/tutorials/devika-tutorial/07-debugging-and-troubleshooting.md
index 4b5279ec..42adce00 100644
--- a/tutorials/devika-tutorial/07-debugging-and-troubleshooting.md
+++ b/tutorials/devika-tutorial/07-debugging-and-troubleshooting.md
@@ -226,3 +226,363 @@ Devika's multi-agent pipeline creates multiple potential failure points that are
 - [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)
+
+### Scenario Playbook 1: Chapter 7: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Debugging and Troubleshooting
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/devika-tutorial/08-production-operations-and-governance.md b/tutorials/devika-tutorial/08-production-operations-and-governance.md
index 073a7330..254b4455 100644
--- a/tutorials/devika-tutorial/08-production-operations-and-governance.md
+++ b/tutorials/devika-tutorial/08-production-operations-and-governance.md
@@ -225,3 +225,363 @@ Devika's production governance framework solves the accountability and blast-rad
 - [Previous Chapter: Chapter 7: Debugging and Troubleshooting](07-debugging-and-troubleshooting.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 8: Production Operations and Governance
+
+- tutorial context: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Devika Tutorial: Open-Source Autonomous AI Software Engineer**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/dify-platform-deep-dive/01-system-overview.md b/tutorials/dify-platform-deep-dive/01-system-overview.md
index e4c01f6b..f4d272f8 100644
--- a/tutorials/dify-platform-deep-dive/01-system-overview.md
+++ b/tutorials/dify-platform-deep-dive/01-system-overview.md
@@ -298,3 +298,289 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 2: Core Architecture](02-core-architecture.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Dify Platform: Deep Dive Tutorial**
+- tutorial slug: **dify-platform-deep-dive**
+- chapter focus: **Chapter 1: Dify System Overview**
+- system context: **Dify Platform Deep Dive**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Dify System Overview`.
+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
+
+- [Dify](https://github.com/langgenius/dify)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Dify System Overview`.
+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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Dify System Overview
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/dify-platform-deep-dive/02-core-architecture.md b/tutorials/dify-platform-deep-dive/02-core-architecture.md
index 54040d52..5d3ee15d 100644
--- a/tutorials/dify-platform-deep-dive/02-core-architecture.md
+++ b/tutorials/dify-platform-deep-dive/02-core-architecture.md
@@ -475,3 +475,109 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 3: Workflow Engine](03-workflow-engine.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Dify Platform: Deep Dive Tutorial**
+- tutorial slug: **dify-platform-deep-dive**
+- chapter focus: **Chapter 2: Core Architecture**
+- system context: **Dify Platform Deep Dive**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Core 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
+
+- [Dify](https://github.com/langgenius/dify)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Core 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: Core Architecture
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/dify-platform-deep-dive/08-operations-playbook.md b/tutorials/dify-platform-deep-dive/08-operations-playbook.md
index 8414171e..8cad92b0 100644
--- a/tutorials/dify-platform-deep-dive/08-operations-playbook.md
+++ b/tutorials/dify-platform-deep-dive/08-operations-playbook.md
@@ -84,3 +84,505 @@ Suggested trace strategy:
 - [Previous Chapter: Chapter 7: Production Deployment](07-production-deployment.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Dify Platform: Deep Dive Tutorial**
+- tutorial slug: **dify-platform-deep-dive**
+- chapter focus: **Chapter 8: Operations Playbook**
+- system context: **Dify Platform Deep Dive**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Operations Playbook`.
+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
+
+- [Dify](https://github.com/langgenius/dify)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Operations Playbook`.
+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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Operations Playbook
+
+- tutorial context: **Dify Platform: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/dify-platform-deep-dive/index.md b/tutorials/dify-platform-deep-dive/index.md
index ba9cd1a3..cad278d5 100644
--- a/tutorials/dify-platform-deep-dive/index.md
+++ b/tutorials/dify-platform-deep-dive/index.md
@@ -3,6 +3,7 @@ layout: default
 title: "Dify Platform Deep Dive"
 nav_order: 3
 has_children: true
+format_version: v2
 ---
 
 # Dify Platform: Deep Dive Tutorial
@@ -13,6 +14,16 @@ has_children: true
 [![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 [![Python](https://img.shields.io/badge/Python-Flask-blue)](https://github.com/langgenius/dify)
 
+## Why This Track Matters
+
+Dify provides a complete open-source platform for building LLM applications with a visual workflow editor, RAG pipeline, and agent framework — reducing the time from idea to deployed AI application.
+
+This track focuses on:
+- building and deploying LLM workflows with Dify's drag-and-drop node system
+- implementing RAG pipelines with multi-stage document processing and vector search
+- orchestrating agents with tool-calling loops and reasoning chain management
+- operating Dify in production with Docker, monitoring, and security controls
+
 ## What Is Dify?
 
 Dify is an open-source LLM application platform that provides a visual interface for building AI workflows, RAG systems, and agent frameworks. It supports orchestrating complex LLM pipelines with a drag-and-drop node system and offers one-click deployment via Docker.
@@ -26,7 +37,7 @@ Dify is an open-source LLM application platform that provides a visual interface
 | **Plugin System** | Extensible architecture for custom nodes and integrations |
 | **Deployment** | One-click Docker Compose deployment |
 
-## Architecture Overview
+## Mental Model
 
 ```mermaid
 graph TB
@@ -61,7 +72,7 @@ graph TB
     Backend --> LLM
 ```
 
-## Tutorial Structure
+## Chapter Guide
 
 | Chapter | Topic | What You'll Learn |
 |---------|-------|-------------------|
@@ -112,6 +123,19 @@ Ready to begin? Start with [Chapter 1: System Overview](01-system-overview.md).
 7. [Chapter 7: Production Deployment](07-production-deployment.md)
 8. [Chapter 8: Operations Playbook](08-operations-playbook.md)
 
+## Current Snapshot (auto-updated)
+
+- repository: [langgenius/dify](https://github.com/langgenius/dify)
+- stars: about **68K**
+- project positioning: leading open-source LLM application development platform
+
+## What You Will Learn
+
+- how Dify's workflow engine executes node graphs and manages LLM pipeline state
+- how to implement multi-stage RAG with document processing, embeddings, and vector retrieval
+- how Dify's agent framework manages tool-calling loops and reasoning chains
+- how to deploy and operate Dify in production with Docker Compose and monitoring
+
 ## Source References
 
 - [Dify](https://github.com/langgenius/dify)
diff --git a/tutorials/flowise-llm-orchestration/01-system-overview.md b/tutorials/flowise-llm-orchestration/01-system-overview.md
index 1c12fe89..07051ac7 100644
--- a/tutorials/flowise-llm-orchestration/01-system-overview.md
+++ b/tutorials/flowise-llm-orchestration/01-system-overview.md
@@ -563,3 +563,97 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 2: Workflow Engine](02-workflow-engine.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- tutorial slug: **flowise-llm-orchestration**
+- chapter focus: **Chapter 1: Flowise System Overview**
+- system context: **Flowise Llm Orchestration**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Flowise System Overview`.
+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
+
+- [Flowise](https://github.com/FlowiseAI/Flowise)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Flowise System Overview`.
+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?
diff --git a/tutorials/flowise-llm-orchestration/06-security-governance.md b/tutorials/flowise-llm-orchestration/06-security-governance.md
index 41b62a7c..10d77c79 100644
--- a/tutorials/flowise-llm-orchestration/06-security-governance.md
+++ b/tutorials/flowise-llm-orchestration/06-security-governance.md
@@ -104,3 +104,481 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 7: Observability](07-observability.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- tutorial slug: **flowise-llm-orchestration**
+- chapter focus: **Chapter 6: Security and Governance**
+- system context: **Flowise Llm Orchestration**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Security 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
+
+- [Flowise](https://github.com/FlowiseAI/Flowise)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Security 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 6: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Security and Governance
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/flowise-llm-orchestration/07-observability.md b/tutorials/flowise-llm-orchestration/07-observability.md
index 5420876c..be9a6ba4 100644
--- a/tutorials/flowise-llm-orchestration/07-observability.md
+++ b/tutorials/flowise-llm-orchestration/07-observability.md
@@ -101,3 +101,481 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 8: Extension Ecosystem](08-extension-ecosystem.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- tutorial slug: **flowise-llm-orchestration**
+- chapter focus: **Chapter 7: Observability**
+- system context: **Flowise Llm Orchestration**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: 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
+
+- [Flowise](https://github.com/FlowiseAI/Flowise)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Observability
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/flowise-llm-orchestration/08-extension-ecosystem.md b/tutorials/flowise-llm-orchestration/08-extension-ecosystem.md
index 40c5efda..8962df8e 100644
--- a/tutorials/flowise-llm-orchestration/08-extension-ecosystem.md
+++ b/tutorials/flowise-llm-orchestration/08-extension-ecosystem.md
@@ -93,3 +93,493 @@ Suggested trace strategy:
 - [Previous Chapter: Chapter 7: Observability](07-observability.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- tutorial slug: **flowise-llm-orchestration**
+- chapter focus: **Chapter 8: Extension Ecosystem**
+- system context: **Flowise Llm Orchestration**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Extension 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
+
+- [Flowise](https://github.com/FlowiseAI/Flowise)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Extension 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 8: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Extension Ecosystem
+
+- tutorial context: **Flowise LLM Orchestration: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/flowise-llm-orchestration/index.md b/tutorials/flowise-llm-orchestration/index.md
index 3c0f161e..2056edd0 100644
--- a/tutorials/flowise-llm-orchestration/index.md
+++ b/tutorials/flowise-llm-orchestration/index.md
@@ -3,6 +3,7 @@ layout: default
 title: "Flowise LLM Orchestration"
 nav_order: 4
 has_children: true
+format_version: v2
 ---
 
 # Flowise LLM Orchestration: Deep Dive Tutorial
@@ -13,6 +14,16 @@ has_children: true
 [![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 [![Node.js](https://img.shields.io/badge/Node.js-React-green)](https://github.com/FlowiseAI/Flowise)
 
+## Why This Track Matters
+
+Flowise makes LLM orchestration visual and accessible — a drag-and-drop canvas for building production pipelines without boilerplate, with auto-generated APIs for every workflow you create.
+
+This track focuses on:
+- building LLM workflows visually with Flowise's node canvas
+- developing custom nodes to extend Flowise with new integrations
+- connecting LLM providers, vector stores, and tools in production pipelines
+- deploying and monitoring Flowise workflows with Docker
+
 ## What Is Flowise?
 
 Flowise is an open-source visual workflow builder for LLM applications. It provides a drag-and-drop canvas for connecting AI models, data sources, and tools into production-ready pipelines — without writing boilerplate code.
@@ -26,7 +37,7 @@ Flowise is an open-source visual workflow builder for LLM applications. It provi
 | **Custom Nodes** | Extensible architecture for building custom integrations |
 | **API Export** | Auto-generated REST APIs for every workflow |
 
-## Architecture Overview
+## Mental Model
 
 ```mermaid
 graph TB
@@ -53,7 +64,7 @@ graph TB
     ENGINE --> Integrations
 ```
 
-## Tutorial Structure
+## Chapter Guide
 
 | Chapter | Topic | What You'll Learn |
 |---------|-------|-------------------|
@@ -103,6 +114,19 @@ Ready to begin? Start with [Chapter 1: System Overview](01-system-overview.md).
 7. [Chapter 7: Observability](07-observability.md)
 8. [Chapter 8: Extension Ecosystem](08-extension-ecosystem.md)
 
+## Current Snapshot (auto-updated)
+
+- repository: [FlowiseAI/Flowise](https://github.com/FlowiseAI/Flowise)
+- stars: about **34K**
+- project positioning: popular open-source visual LLM workflow builder with 100+ pre-built nodes
+
+## What You Will Learn
+
+- how Flowise's node graph execution engine processes data flow and streaming responses
+- how to build custom nodes with typed inputs and outputs for new integrations
+- how to connect LLM providers, vector stores, and external tools in visual workflows
+- how to deploy Flowise with Docker and manage security, governance, and observability
+
 ## Source References
 
 - [Flowise](https://github.com/FlowiseAI/Flowise)
diff --git a/tutorials/hapi-tutorial/01-getting-started.md b/tutorials/hapi-tutorial/01-getting-started.md
index 05dce306..38c06436 100644
--- a/tutorials/hapi-tutorial/01-getting-started.md
+++ b/tutorials/hapi-tutorial/01-getting-started.md
@@ -98,3 +98,486 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 2: System Architecture](02-system-architecture.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- tutorial slug: **hapi-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Hapi 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
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+- [HAPI Docs](https://hapi.run)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/hapi-tutorial/02-system-architecture.md b/tutorials/hapi-tutorial/02-system-architecture.md
index 6cfb90b8..c0fc2da0 100644
--- a/tutorials/hapi-tutorial/02-system-architecture.md
+++ b/tutorials/hapi-tutorial/02-system-architecture.md
@@ -93,3 +93,498 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 3: Session Lifecycle and Handoff](03-session-lifecycle-and-handoff.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- tutorial slug: **hapi-tutorial**
+- chapter focus: **Chapter 2: System Architecture**
+- system context: **Hapi 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
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+- [HAPI Docs](https://hapi.run)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-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: 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/hapi-tutorial/03-session-lifecycle-and-handoff.md b/tutorials/hapi-tutorial/03-session-lifecycle-and-handoff.md
index 6a2d36ac..93211da5 100644
--- a/tutorials/hapi-tutorial/03-session-lifecycle-and-handoff.md
+++ b/tutorials/hapi-tutorial/03-session-lifecycle-and-handoff.md
@@ -91,3 +91,498 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 4: Remote Access and Networking](04-remote-access-and-networking.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- tutorial slug: **hapi-tutorial**
+- chapter focus: **Chapter 3: Session Lifecycle and Handoff**
+- system context: **Hapi Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Session Lifecycle and Handoff`.
+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
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+- [HAPI Docs](https://hapi.run)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-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: Session Lifecycle and Handoff`.
+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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Session Lifecycle and Handoff
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/hapi-tutorial/04-remote-access-and-networking.md b/tutorials/hapi-tutorial/04-remote-access-and-networking.md
index a3dace6b..564ceac1 100644
--- a/tutorials/hapi-tutorial/04-remote-access-and-networking.md
+++ b/tutorials/hapi-tutorial/04-remote-access-and-networking.md
@@ -85,3 +85,498 @@ Use the following upstream sources to verify implementation details while readin
 - [Next Chapter: Chapter 5: Permissions and Approval Workflow](05-permissions-and-approval-workflow.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- tutorial slug: **hapi-tutorial**
+- chapter focus: **Chapter 4: Remote Access and Networking**
+- system context: **Hapi Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Remote Access and Networking`.
+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
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+- [HAPI Docs](https://hapi.run)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-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: Remote Access and Networking`.
+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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Remote Access and Networking
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/hapi-tutorial/05-permissions-and-approval-workflow.md b/tutorials/hapi-tutorial/05-permissions-and-approval-workflow.md
index 0b9beecd..9e8d6f1a 100644
--- a/tutorials/hapi-tutorial/05-permissions-and-approval-workflow.md
+++ b/tutorials/hapi-tutorial/05-permissions-and-approval-workflow.md
@@ -85,3 +85,498 @@ Use the following upstream sources to verify implementation details while readin
 - [Next Chapter: Chapter 6: PWA, Telegram, and Extensions](06-pwa-telegram-and-extensions.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- tutorial slug: **hapi-tutorial**
+- chapter focus: **Chapter 5: Permissions and Approval Workflow**
+- system context: **Hapi Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Permissions and Approval 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
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+- [HAPI Docs](https://hapi.run)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-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: Permissions and Approval 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Permissions and Approval Workflow
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/hapi-tutorial/06-pwa-telegram-and-extensions.md b/tutorials/hapi-tutorial/06-pwa-telegram-and-extensions.md
index be0bfb2e..5d9d3ea9 100644
--- a/tutorials/hapi-tutorial/06-pwa-telegram-and-extensions.md
+++ b/tutorials/hapi-tutorial/06-pwa-telegram-and-extensions.md
@@ -81,3 +81,510 @@ Use the following upstream sources to verify implementation details while readin
 - [Next Chapter: Chapter 7: Configuration and Security](07-configuration-and-security.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- tutorial slug: **hapi-tutorial**
+- chapter focus: **Chapter 6: PWA, Telegram, and Extensions**
+- system context: **Hapi Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: PWA, Telegram, and 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
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+- [HAPI Docs](https://hapi.run)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-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: PWA, Telegram, and 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: PWA, Telegram, and Extensions
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/hapi-tutorial/07-configuration-and-security.md b/tutorials/hapi-tutorial/07-configuration-and-security.md
index 34afadec..ffe786d0 100644
--- a/tutorials/hapi-tutorial/07-configuration-and-security.md
+++ b/tutorials/hapi-tutorial/07-configuration-and-security.md
@@ -85,3 +85,498 @@ Use the following upstream sources to verify implementation details while readin
 - [Next Chapter: Chapter 8: Production Operations](08-production-operations.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- tutorial slug: **hapi-tutorial**
+- chapter focus: **Chapter 7: Configuration and Security**
+- system context: **Hapi Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Configuration 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
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+- [HAPI Docs](https://hapi.run)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-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: Configuration 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 7: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Configuration and Security
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/hapi-tutorial/08-production-operations.md b/tutorials/hapi-tutorial/08-production-operations.md
index d0c5fc1e..a73b4235 100644
--- a/tutorials/hapi-tutorial/08-production-operations.md
+++ b/tutorials/hapi-tutorial/08-production-operations.md
@@ -88,3 +88,498 @@ Use the following upstream sources to verify implementation details while readin
 - [Previous Chapter: Chapter 7: Configuration and Security](07-configuration-and-security.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- tutorial slug: **hapi-tutorial**
+- chapter focus: **Chapter 8: Production Operations**
+- system context: **Hapi Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: 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
+
+- [HAPI Repository](https://github.com/tiann/hapi)
+- [HAPI Releases](https://github.com/tiann/hapi/releases)
+- [HAPI Docs](https://hapi.run)
+
+### Cross-Tutorial Connection Map
+
+- [Cline Tutorial](../cline-tutorial/)
+- [Roo Code Tutorial](../roo-code-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 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 Operations
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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
+
+- tutorial context: **HAPI Tutorial: Remote Control for Local AI Coding Sessions**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/hapi-tutorial/index.md b/tutorials/hapi-tutorial/index.md
index a0f8576f..0f755a6f 100644
--- a/tutorials/hapi-tutorial/index.md
+++ b/tutorials/hapi-tutorial/index.md
@@ -3,6 +3,7 @@ layout: default
 title: "HAPI Tutorial"
 nav_order: 100
 has_children: true
+format_version: v2
 ---
 
 # HAPI Tutorial: Remote Control for Local AI Coding Sessions
@@ -13,6 +14,16 @@ has_children: true
 [![License](https://img.shields.io/badge/License-AGPL_3.0-blue.svg)](https://opensource.org/licenses/AGPL-3.0)
 [![Docs](https://img.shields.io/badge/Docs-hapi.run-orange)](https://hapi.run)
 
+## Why This Track Matters
+
+HAPI solves the remote oversight problem for local AI coding sessions — you can run Claude Code or other agents on your laptop while monitoring, approving, and controlling them from a phone or browser anywhere.
+
+This track focuses on:
+- setting up local-first AI coding sessions with remote control capability
+- designing safe approval policies for agent tool access
+- operating HAPI across multiple machines and networks
+- hardening and monitoring HAPI for team usage
+
 ## What is HAPI?
 
 HAPI wraps existing coding agents and adds a hub/web control plane so sessions can be handed off between terminal and phone/browser without restarting context.
@@ -25,7 +36,7 @@ HAPI wraps existing coding agents and adds a hub/web control plane so sessions c
 - license: AGPL-3.0
 - key capabilities: remote approvals, PWA control, Telegram integration, multi-machine session routing
 
-## Tutorial Chapters
+## Chapter Guide
 
 1. **[Chapter 1: Getting Started](01-getting-started.md)** - install HAPI, start hub, and launch first wrapped agent session
 2. **[Chapter 2: System Architecture](02-system-architecture.md)** - CLI, hub, web app, and protocol boundaries
@@ -79,7 +90,7 @@ Ready to begin? Continue to [Chapter 1: Getting Started](01-getting-started.md).
 7. [Chapter 7: Configuration and Security](07-configuration-and-security.md)
 8. [Chapter 8: Production Operations](08-production-operations.md)
 
-## Concept Flow
+## Mental Model
 
 ```mermaid
 flowchart TD
diff --git a/tutorials/kiro-tutorial/01-getting-started.md b/tutorials/kiro-tutorial/01-getting-started.md
index 6aea7d16..9870df67 100644
--- a/tutorials/kiro-tutorial/01-getting-started.md
+++ b/tutorials/kiro-tutorial/01-getting-started.md
@@ -314,3 +314,267 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 2: Spec-Driven Development Workflow](02-spec-driven-development-workflow.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 1: Getting Started
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/kiro-tutorial/02-spec-driven-development-workflow.md b/tutorials/kiro-tutorial/02-spec-driven-development-workflow.md
index 8b81f5ab..2cc5744e 100644
--- a/tutorials/kiro-tutorial/02-spec-driven-development-workflow.md
+++ b/tutorials/kiro-tutorial/02-spec-driven-development-workflow.md
@@ -388,3 +388,195 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 3: Agent Steering and Rules Configuration](03-agent-steering-and-rules-configuration.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 2: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Spec-Driven Development Workflow
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/kiro-tutorial/03-agent-steering-and-rules-configuration.md b/tutorials/kiro-tutorial/03-agent-steering-and-rules-configuration.md
index 962893c1..85b6d409 100644
--- a/tutorials/kiro-tutorial/03-agent-steering-and-rules-configuration.md
+++ b/tutorials/kiro-tutorial/03-agent-steering-and-rules-configuration.md
@@ -385,3 +385,207 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 4: Autonomous Agent Mode](04-autonomous-agent-mode.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 3: Agent Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Steering and Rules Configuration
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/kiro-tutorial/04-autonomous-agent-mode.md b/tutorials/kiro-tutorial/04-autonomous-agent-mode.md
index da320b1e..d7a8d902 100644
--- a/tutorials/kiro-tutorial/04-autonomous-agent-mode.md
+++ b/tutorials/kiro-tutorial/04-autonomous-agent-mode.md
@@ -386,3 +386,195 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 5: MCP Integration and External Tools](05-mcp-integration-and-external-tools.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 4: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Autonomous Agent Mode
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/kiro-tutorial/05-mcp-integration-and-external-tools.md b/tutorials/kiro-tutorial/05-mcp-integration-and-external-tools.md
index 374f6922..7cda6a71 100644
--- a/tutorials/kiro-tutorial/05-mcp-integration-and-external-tools.md
+++ b/tutorials/kiro-tutorial/05-mcp-integration-and-external-tools.md
@@ -431,3 +431,159 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 6: Hooks and Automation](06-hooks-and-automation.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 5: MCP Integration and External Tools
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: MCP Integration and External Tools
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: MCP Integration and External Tools
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: MCP Integration and External Tools
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: MCP Integration and External Tools
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: MCP Integration and External Tools
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: MCP Integration and External Tools
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: MCP Integration and External Tools
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: MCP Integration and External Tools
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: MCP Integration and External Tools
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: MCP Integration and External Tools
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: MCP Integration and External Tools
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: MCP Integration and External Tools
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/kiro-tutorial/06-hooks-and-automation.md b/tutorials/kiro-tutorial/06-hooks-and-automation.md
index 530b72f4..34186932 100644
--- a/tutorials/kiro-tutorial/06-hooks-and-automation.md
+++ b/tutorials/kiro-tutorial/06-hooks-and-automation.md
@@ -419,3 +419,171 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 7: Multi-Model Strategy and Providers](07-multi-model-strategy-and-providers.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 6: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Hooks and Automation
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/kiro-tutorial/07-multi-model-strategy-and-providers.md b/tutorials/kiro-tutorial/07-multi-model-strategy-and-providers.md
index d7aa97b2..a83669ab 100644
--- a/tutorials/kiro-tutorial/07-multi-model-strategy-and-providers.md
+++ b/tutorials/kiro-tutorial/07-multi-model-strategy-and-providers.md
@@ -388,3 +388,195 @@ Suggested trace strategy:
 - [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)
+
+### Scenario Playbook 1: Chapter 7: Multi-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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-Model Strategy and Providers
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/kiro-tutorial/08-team-operations-and-governance.md b/tutorials/kiro-tutorial/08-team-operations-and-governance.md
index 80e0f131..a6fa4e65 100644
--- a/tutorials/kiro-tutorial/08-team-operations-and-governance.md
+++ b/tutorials/kiro-tutorial/08-team-operations-and-governance.md
@@ -429,3 +429,159 @@ Suggested trace strategy:
 - [Tutorial Index](index.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.md)
+
+### Scenario Playbook 1: Chapter 8: Team Operations and Governance
+
+- tutorial context: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Kiro Tutorial: Spec-Driven Agentic IDE from AWS**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/logseq-knowledge-management/01-knowledge-management-principles.md b/tutorials/logseq-knowledge-management/01-knowledge-management-principles.md
index d6f332a4..8814ebb5 100644
--- a/tutorials/logseq-knowledge-management/01-knowledge-management-principles.md
+++ b/tutorials/logseq-knowledge-management/01-knowledge-management-principles.md
@@ -523,3 +523,97 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 2: System Architecture](02-system-architecture.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Logseq: Deep Dive Tutorial**
+- tutorial slug: **logseq-knowledge-management**
+- chapter focus: **Chapter 1: Knowledge Management Philosophy**
+- system context: **Logseq Knowledge Management**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Knowledge Management Philosophy`.
+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
+
+- [Logseq](https://github.com/logseq/logseq)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Knowledge Management Philosophy`.
+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?
diff --git a/tutorials/logseq-knowledge-management/02-system-architecture.md b/tutorials/logseq-knowledge-management/02-system-architecture.md
index 7e08a839..e3de4768 100644
--- a/tutorials/logseq-knowledge-management/02-system-architecture.md
+++ b/tutorials/logseq-knowledge-management/02-system-architecture.md
@@ -92,3 +92,493 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 3: Local-First Data](03-local-first-data.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Logseq: Deep Dive Tutorial**
+- tutorial slug: **logseq-knowledge-management**
+- chapter focus: **Chapter 2: System Architecture**
+- system context: **Logseq Knowledge Management**
+- 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
+
+- [Logseq](https://github.com/logseq/logseq)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/logseq-knowledge-management/03-local-first-data.md b/tutorials/logseq-knowledge-management/03-local-first-data.md
index 387ca008..87281242 100644
--- a/tutorials/logseq-knowledge-management/03-local-first-data.md
+++ b/tutorials/logseq-knowledge-management/03-local-first-data.md
@@ -93,3 +93,493 @@ Suggested trace strategy:
 - [Next Chapter: Logseq Development Environment Setup](04-development-setup.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Logseq: Deep Dive Tutorial**
+- tutorial slug: **logseq-knowledge-management**
+- chapter focus: **Chapter 3: Local-First Data**
+- system context: **Logseq Knowledge Management**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Local-First Data`.
+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
+
+- [Logseq](https://github.com/logseq/logseq)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Local-First Data`.
+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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Local-First Data
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/logseq-knowledge-management/05-block-data-model.md b/tutorials/logseq-knowledge-management/05-block-data-model.md
index 0266a177..519e675d 100644
--- a/tutorials/logseq-knowledge-management/05-block-data-model.md
+++ b/tutorials/logseq-knowledge-management/05-block-data-model.md
@@ -97,3 +97,493 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 6: Block Editor](06-block-editor.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Logseq: Deep Dive Tutorial**
+- tutorial slug: **logseq-knowledge-management**
+- chapter focus: **Chapter 5: Block Data Model**
+- system context: **Logseq Knowledge Management**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Block Data 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
+
+- [Logseq](https://github.com/logseq/logseq)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Block Data 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 5: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Data Model
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/logseq-knowledge-management/06-block-editor.md b/tutorials/logseq-knowledge-management/06-block-editor.md
index 43c5f908..cd7504a1 100644
--- a/tutorials/logseq-knowledge-management/06-block-editor.md
+++ b/tutorials/logseq-knowledge-management/06-block-editor.md
@@ -93,3 +93,493 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 7: Bi-Directional Links](07-bidirectional-links.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Logseq: Deep Dive Tutorial**
+- tutorial slug: **logseq-knowledge-management**
+- chapter focus: **Chapter 6: Block Editor**
+- system context: **Logseq Knowledge Management**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Block Editor`.
+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
+
+- [Logseq](https://github.com/logseq/logseq)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Block Editor`.
+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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Block Editor
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/logseq-knowledge-management/07-bidirectional-links.md b/tutorials/logseq-knowledge-management/07-bidirectional-links.md
index 0e40da6d..35e8ad06 100644
--- a/tutorials/logseq-knowledge-management/07-bidirectional-links.md
+++ b/tutorials/logseq-knowledge-management/07-bidirectional-links.md
@@ -89,3 +89,493 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 8: Graph Visualization](08-graph-visualization.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Logseq: Deep Dive Tutorial**
+- tutorial slug: **logseq-knowledge-management**
+- chapter focus: **Chapter 7: Bi-Directional Links**
+- system context: **Logseq Knowledge Management**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Bi-Directional Links`.
+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
+
+- [Logseq](https://github.com/logseq/logseq)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Bi-Directional Links`.
+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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Bi-Directional Links
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/logseq-knowledge-management/08-graph-visualization.md b/tutorials/logseq-knowledge-management/08-graph-visualization.md
index 57eea739..5d340c7a 100644
--- a/tutorials/logseq-knowledge-management/08-graph-visualization.md
+++ b/tutorials/logseq-knowledge-management/08-graph-visualization.md
@@ -95,3 +95,493 @@ Suggested trace strategy:
 - [Previous Chapter: Chapter 7: Bi-Directional Links](07-bidirectional-links.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Logseq: Deep Dive Tutorial**
+- tutorial slug: **logseq-knowledge-management**
+- chapter focus: **Chapter 8: Graph Visualization**
+- system context: **Logseq Knowledge Management**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Graph Visualization`.
+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
+
+- [Logseq](https://github.com/logseq/logseq)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Graph Visualization`.
+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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Graph Visualization
+
+- tutorial context: **Logseq: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/logseq-knowledge-management/index.md b/tutorials/logseq-knowledge-management/index.md
index 0e64e734..85eff08e 100644
--- a/tutorials/logseq-knowledge-management/index.md
+++ b/tutorials/logseq-knowledge-management/index.md
@@ -3,6 +3,7 @@ layout: default
 title: "Logseq Knowledge Management"
 nav_order: 40
 has_children: true
+format_version: v2
 ---
 
 # Logseq: Deep Dive Tutorial
@@ -13,6 +14,16 @@ has_children: true
 [![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
 [![ClojureScript](https://img.shields.io/badge/ClojureScript-Electron-purple)](https://github.com/logseq/logseq)
 
+## Why This Track Matters
+
+Logseq proves that a local-first, privacy-preserving knowledge system can be as powerful as cloud-based alternatives — all notes stay as plain Markdown files you own, with a rich graph visualization layer on top.
+
+This track focuses on:
+- understanding block-based editing with bi-directional linking
+- working with Datascript and ClojureScript for local-first data management
+- building knowledge graph visualizations with D3.js
+- operating and extending Logseq with its JavaScript plugin API
+
 ## What Is Logseq?
 
 Logseq is a local-first, privacy-preserving knowledge management platform built with ClojureScript and Electron. It stores notes as plain Markdown/Org-mode files on your filesystem, provides block-based editing with bi-directional linking, and visualizes your knowledge as an interactive graph.
@@ -26,7 +37,7 @@ Logseq is a local-first, privacy-preserving knowledge management platform built
 | **Plugin System** | JavaScript plugin API with sandboxed execution |
 | **Git Sync** | Built-in Git-based synchronization across devices |
 
-## Architecture Overview
+## Mental Model
 
 ```mermaid
 graph TB
@@ -53,7 +64,7 @@ graph TB
     Core --> Storage
 ```
 
-## Tutorial Structure
+## Chapter Guide
 
 | Chapter | Topic | What You'll Learn |
 |---------|-------|-------------------|
@@ -105,6 +116,19 @@ Ready to begin? Start with [Chapter 1: Knowledge Management Principles](01-knowl
 7. [Chapter 7: Bi-Directional Links](07-bidirectional-links.md)
 8. [Chapter 8: Graph Visualization](08-graph-visualization.md)
 
+## Current Snapshot (auto-updated)
+
+- repository: [logseq/logseq](https://github.com/logseq/logseq)
+- stars: about **32K**
+- project positioning: privacy-first, local-first knowledge management platform with graph visualization
+
+## What You Will Learn
+
+- how Logseq stores notes as plain Markdown files with Datascript indexing for fast queries
+- how block identity, hierarchy, and bi-directional links are managed in the graph model
+- how ClojureScript and Re-frame power the local-first state management architecture
+- how the graph visualization renders large knowledge networks with D3.js
+
 ## Source References
 
 - [Logseq](https://github.com/logseq/logseq)
diff --git a/tutorials/mcp-servers-tutorial/01-getting-started.md b/tutorials/mcp-servers-tutorial/01-getting-started.md
index 77255854..e47d4e5d 100644
--- a/tutorials/mcp-servers-tutorial/01-getting-started.md
+++ b/tutorials/mcp-servers-tutorial/01-getting-started.md
@@ -112,3 +112,473 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 2: Filesystem Server](02-filesystem-server.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- tutorial slug: **mcp-servers-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Mcp Servers 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
+
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [n8n MCP Tutorial](../n8n-mcp-tutorial/)
+- [Claude Code Tutorial - MCP chapter](../claude-code-tutorial/07-mcp.md)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/mcp-servers-tutorial/02-filesystem-server.md b/tutorials/mcp-servers-tutorial/02-filesystem-server.md
index 21ad397b..ab6fa8ca 100644
--- a/tutorials/mcp-servers-tutorial/02-filesystem-server.md
+++ b/tutorials/mcp-servers-tutorial/02-filesystem-server.md
@@ -121,3 +121,461 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 3: Git Server](03-git-server.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- tutorial slug: **mcp-servers-tutorial**
+- chapter focus: **Chapter 2: Filesystem Server**
+- system context: **Mcp Servers Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Filesystem 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
+
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [n8n MCP Tutorial](../n8n-mcp-tutorial/)
+- [Claude Code Tutorial - MCP chapter](../claude-code-tutorial/07-mcp.md)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Filesystem 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 2: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Filesystem Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/mcp-servers-tutorial/03-git-server.md b/tutorials/mcp-servers-tutorial/03-git-server.md
index fee41a20..6d662d6c 100644
--- a/tutorials/mcp-servers-tutorial/03-git-server.md
+++ b/tutorials/mcp-servers-tutorial/03-git-server.md
@@ -112,3 +112,473 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 4: Memory Server](04-memory-server.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- tutorial slug: **mcp-servers-tutorial**
+- chapter focus: **Chapter 3: Git Server**
+- system context: **Mcp Servers Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Git 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
+
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [n8n MCP Tutorial](../n8n-mcp-tutorial/)
+- [Claude Code Tutorial - MCP chapter](../claude-code-tutorial/07-mcp.md)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Git 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 3: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Git Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/mcp-servers-tutorial/04-memory-server.md b/tutorials/mcp-servers-tutorial/04-memory-server.md
index 036e643b..cd93cc55 100644
--- a/tutorials/mcp-servers-tutorial/04-memory-server.md
+++ b/tutorials/mcp-servers-tutorial/04-memory-server.md
@@ -112,3 +112,473 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 5: Multi-Language Servers](05-multi-language-servers.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- tutorial slug: **mcp-servers-tutorial**
+- chapter focus: **Chapter 4: Memory Server**
+- system context: **Mcp Servers Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Memory 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
+
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [n8n MCP Tutorial](../n8n-mcp-tutorial/)
+- [Claude Code Tutorial - MCP chapter](../claude-code-tutorial/07-mcp.md)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Memory 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 4: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Memory Server
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/mcp-servers-tutorial/05-multi-language-servers.md b/tutorials/mcp-servers-tutorial/05-multi-language-servers.md
index 21cad76d..afd890d3 100644
--- a/tutorials/mcp-servers-tutorial/05-multi-language-servers.md
+++ b/tutorials/mcp-servers-tutorial/05-multi-language-servers.md
@@ -101,3 +101,485 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 6: Custom Server Development](06-custom-server-development.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- tutorial slug: **mcp-servers-tutorial**
+- chapter focus: **Chapter 5: Multi-Language Servers**
+- system context: **Mcp Servers Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Multi-Language 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
+
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [n8n MCP Tutorial](../n8n-mcp-tutorial/)
+- [Claude Code Tutorial - MCP chapter](../claude-code-tutorial/07-mcp.md)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Multi-Language 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 5: Multi-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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-Language Servers
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/mcp-servers-tutorial/06-custom-server-development.md b/tutorials/mcp-servers-tutorial/06-custom-server-development.md
index 943323b9..05d0192d 100644
--- a/tutorials/mcp-servers-tutorial/06-custom-server-development.md
+++ b/tutorials/mcp-servers-tutorial/06-custom-server-development.md
@@ -115,3 +115,473 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 7: Security Considerations](07-security-considerations.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- tutorial slug: **mcp-servers-tutorial**
+- chapter focus: **Chapter 6: Custom Server Development**
+- system context: **Mcp Servers Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Custom Server Development`.
+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
+
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [n8n MCP Tutorial](../n8n-mcp-tutorial/)
+- [Claude Code Tutorial - MCP chapter](../claude-code-tutorial/07-mcp.md)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Custom Server Development`.
+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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Server Development
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/mcp-servers-tutorial/07-security-considerations.md b/tutorials/mcp-servers-tutorial/07-security-considerations.md
index 09e7b2f3..d363d5f8 100644
--- a/tutorials/mcp-servers-tutorial/07-security-considerations.md
+++ b/tutorials/mcp-servers-tutorial/07-security-considerations.md
@@ -105,3 +105,485 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 8: Production Adaptation](08-production-adaptation.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- tutorial slug: **mcp-servers-tutorial**
+- chapter focus: **Chapter 7: Security Considerations**
+- system context: **Mcp Servers Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Security Considerations`.
+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
+
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [n8n MCP Tutorial](../n8n-mcp-tutorial/)
+- [Claude Code Tutorial - MCP chapter](../claude-code-tutorial/07-mcp.md)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Security Considerations`.
+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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Considerations
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/mcp-servers-tutorial/08-production-adaptation.md b/tutorials/mcp-servers-tutorial/08-production-adaptation.md
index ed12e8d7..6e8d6430 100644
--- a/tutorials/mcp-servers-tutorial/08-production-adaptation.md
+++ b/tutorials/mcp-servers-tutorial/08-production-adaptation.md
@@ -111,3 +111,473 @@ Suggested trace strategy:
 - [Previous Chapter: Chapter 7: Security Considerations](07-security-considerations.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- tutorial slug: **mcp-servers-tutorial**
+- chapter focus: **Chapter 8: Production Adaptation**
+- system context: **Mcp Servers Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Adaptation`.
+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
+
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Cross-Tutorial Connection Map
+
+- [MCP Python SDK Tutorial](../mcp-python-sdk-tutorial/)
+- [Anthropic Skills Tutorial](../anthropic-skills-tutorial/)
+- [n8n MCP Tutorial](../n8n-mcp-tutorial/)
+- [Claude Code Tutorial - MCP chapter](../claude-code-tutorial/07-mcp.md)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [MCP servers repository](https://github.com/modelcontextprotocol/servers)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Adaptation`.
+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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Adaptation
+
+- tutorial context: **MCP Servers Tutorial: Reference Implementations and Patterns**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/mcp-servers-tutorial/index.md b/tutorials/mcp-servers-tutorial/index.md
index e408e8ef..2d02bee0 100644
--- a/tutorials/mcp-servers-tutorial/index.md
+++ b/tutorials/mcp-servers-tutorial/index.md
@@ -3,6 +3,7 @@ layout: default
 title: "MCP Servers Tutorial"
 nav_order: 92
 has_children: true
+format_version: v2
 ---
 
 # MCP Servers Tutorial: Reference Implementations and Patterns
@@ -13,6 +14,16 @@ has_children: true
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Registry](https://img.shields.io/badge/MCP-Registry-blue)](https://registry.modelcontextprotocol.io/)
 
+## Why This Track Matters
+
+The official MCP reference servers are the canonical blueprints for understanding how to implement safe, reliable Model Context Protocol integrations — essential reading before building your own production servers.
+
+This track focuses on:
+- understanding MCP protocol patterns through official reference implementations
+- building safe file, git, memory, and web retrieval integrations
+- applying security controls and least-privilege design to MCP servers
+- hardening reference patterns for production reliability and observability
+
 ## What this repository is for
 
 The official `modelcontextprotocol/servers` repository contains a small set of **reference implementations** maintained by the MCP steering group. These servers demonstrate protocol usage and design patterns.
@@ -34,7 +45,7 @@ Important distinction:
 | Sequential Thinking | Structured iterative reasoning tool interface |
 | Time | Timezone-aware utilities and conversion |
 
-## Tutorial Structure
+## Chapter Guide
 
 | Chapter | Topic | What You Will Learn |
 |:--------|:------|:--------------------|
@@ -98,11 +109,24 @@ Ready to begin? Start with [Chapter 1: Getting Started](01-getting-started.md).
 7. [Chapter 7: Security Considerations](07-security-considerations.md)
 8. [Chapter 8: Production Adaptation](08-production-adaptation.md)
 
+## Current Snapshot (auto-updated)
+
+- repository: [modelcontextprotocol/servers](https://github.com/modelcontextprotocol/servers)
+- stars: about **13K**
+- project positioning: official MCP reference server implementations maintained by the MCP steering group
+
+## What You Will Learn
+
+- how each official reference server demonstrates core MCP protocol patterns
+- how to implement safe file operations with allowlisted roots and path validation
+- how to apply security threat models and least-privilege principles to MCP servers
+- how to adapt reference patterns for production reliability and operational hardening
+
 ## Source References
 
 - [MCP servers repository](https://github.com/modelcontextprotocol/servers)
 
-## Concept Flow
+## Mental Model
 
 ```mermaid
 flowchart TD
diff --git a/tutorials/nocodb-database-platform/01-system-overview.md b/tutorials/nocodb-database-platform/01-system-overview.md
index d5e52c7e..144c7885 100644
--- a/tutorials/nocodb-database-platform/01-system-overview.md
+++ b/tutorials/nocodb-database-platform/01-system-overview.md
@@ -483,3 +483,109 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 2: Database Abstraction Layer](02-database-abstraction.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **NocoDB: Deep Dive Tutorial**
+- tutorial slug: **nocodb-database-platform**
+- chapter focus: **Chapter 1: NocoDB System Overview**
+- system context: **Nocodb Database Platform**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: NocoDB System Overview`.
+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
+
+- [NocoDB](https://github.com/nocodb/nocodb)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: NocoDB System Overview`.
+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: NocoDB System Overview
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/nocodb-database-platform/05-query-builder.md b/tutorials/nocodb-database-platform/05-query-builder.md
index f84f9f6e..4131fa78 100644
--- a/tutorials/nocodb-database-platform/05-query-builder.md
+++ b/tutorials/nocodb-database-platform/05-query-builder.md
@@ -93,3 +93,493 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 6: Auth System](06-auth-system.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **NocoDB: Deep Dive Tutorial**
+- tutorial slug: **nocodb-database-platform**
+- chapter focus: **Chapter 5: Query Builder**
+- system context: **Nocodb Database Platform**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Query Builder`.
+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
+
+- [NocoDB](https://github.com/nocodb/nocodb)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Query Builder`.
+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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Query Builder
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/nocodb-database-platform/06-auth-system.md b/tutorials/nocodb-database-platform/06-auth-system.md
index 074db3c5..1474e593 100644
--- a/tutorials/nocodb-database-platform/06-auth-system.md
+++ b/tutorials/nocodb-database-platform/06-auth-system.md
@@ -94,3 +94,493 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 7: Vue Components](07-vue-components.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **NocoDB: Deep Dive Tutorial**
+- tutorial slug: **nocodb-database-platform**
+- chapter focus: **Chapter 6: Auth System**
+- system context: **Nocodb Database Platform**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Auth System`.
+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
+
+- [NocoDB](https://github.com/nocodb/nocodb)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Auth System`.
+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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Auth System
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/nocodb-database-platform/07-vue-components.md b/tutorials/nocodb-database-platform/07-vue-components.md
index d6fc61dd..ab435505 100644
--- a/tutorials/nocodb-database-platform/07-vue-components.md
+++ b/tutorials/nocodb-database-platform/07-vue-components.md
@@ -92,3 +92,493 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 8: Realtime Features](08-realtime-features.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **NocoDB: Deep Dive Tutorial**
+- tutorial slug: **nocodb-database-platform**
+- chapter focus: **Chapter 7: Vue Components**
+- system context: **Nocodb Database Platform**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Vue 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
+
+- [NocoDB](https://github.com/nocodb/nocodb)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Vue 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 7: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Vue Components
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/nocodb-database-platform/08-realtime-features.md b/tutorials/nocodb-database-platform/08-realtime-features.md
index 93030988..c095eccf 100644
--- a/tutorials/nocodb-database-platform/08-realtime-features.md
+++ b/tutorials/nocodb-database-platform/08-realtime-features.md
@@ -89,3 +89,493 @@ Suggested trace strategy:
 - [Previous Chapter: Chapter 7: Vue Components](07-vue-components.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **NocoDB: Deep Dive Tutorial**
+- tutorial slug: **nocodb-database-platform**
+- chapter focus: **Chapter 8: Realtime Features**
+- system context: **Nocodb Database Platform**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Realtime Features`.
+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
+
+- [NocoDB](https://github.com/nocodb/nocodb)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Realtime Features`.
+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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Features
+
+- tutorial context: **NocoDB: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/nocodb-database-platform/index.md b/tutorials/nocodb-database-platform/index.md
index 1034b94f..fa9ec3c2 100644
--- a/tutorials/nocodb-database-platform/index.md
+++ b/tutorials/nocodb-database-platform/index.md
@@ -3,6 +3,7 @@ layout: default
 title: "NocoDB Database Platform"
 nav_order: 38
 has_children: true
+format_version: v2
 ---
 
 # NocoDB: Deep Dive Tutorial
@@ -13,6 +14,16 @@ has_children: true
 [![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
 [![Node.js](https://img.shields.io/badge/Node.js-Vue.js-green)](https://github.com/nocodb/nocodb)
 
+## Why This Track Matters
+
+NocoDB lets teams build collaborative no-code applications on top of their existing databases without rewriting their data layer — turning any SQL database into an Airtable-like interface with auto-generated APIs.
+
+This track focuses on:
+- connecting NocoDB to MySQL, PostgreSQL, SQLite, and SQL Server
+- understanding automatic REST API generation from database schemas
+- implementing RBAC, authentication, and audit logging
+- deploying NocoDB with Docker for full self-hosted data ownership
+
 ## What Is NocoDB?
 
 NocoDB transforms any SQL database (MySQL, PostgreSQL, SQL Server, SQLite) into a spreadsheet-like interface with auto-generated REST APIs. It provides a no-code layer over existing databases, enabling teams to build applications without rewriting their data layer.
@@ -26,7 +37,7 @@ NocoDB transforms any SQL database (MySQL, PostgreSQL, SQL Server, SQLite) into
 | **Plugin System** | Extensible with custom field types and integrations |
 | **Self-Hosted** | Full Docker deployment, data stays on your infrastructure |
 
-## Architecture Overview
+## Mental Model
 
 ```mermaid
 graph TB
@@ -55,7 +66,7 @@ graph TB
     Backend --> Databases
 ```
 
-## Tutorial Structure
+## Chapter Guide
 
 | Chapter | Topic | What You'll Learn |
 |---------|-------|-------------------|
@@ -105,6 +116,19 @@ Ready to begin? Start with [Chapter 1: System Overview](01-system-overview.md).
 7. [Chapter 7: Vue Components](07-vue-components.md)
 8. [Chapter 8: Realtime Features](08-realtime-features.md)
 
+## Current Snapshot (auto-updated)
+
+- repository: [nocodb/nocodb](https://github.com/nocodb/nocodb)
+- stars: about **48K**
+- project positioning: open-source Airtable alternative built on top of existing SQL databases
+
+## What You Will Learn
+
+- how NocoDB abstracts multiple SQL databases behind a unified spreadsheet-like interface
+- how automatic REST API generation works from existing database schemas
+- how the query builder safely translates UI filters into parameterized SQL
+- how to implement RBAC, configure authentication, and deploy NocoDB with Docker
+
 ## Source References
 
 - [NocoDB](https://github.com/nocodb/nocodb)
diff --git a/tutorials/obsidian-outliner-plugin/01-plugin-architecture.md b/tutorials/obsidian-outliner-plugin/01-plugin-architecture.md
index 5300ba58..4d4d2f3c 100644
--- a/tutorials/obsidian-outliner-plugin/01-plugin-architecture.md
+++ b/tutorials/obsidian-outliner-plugin/01-plugin-architecture.md
@@ -520,3 +520,97 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 2: Text Editing Implementation](02-text-editing.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- tutorial slug: **obsidian-outliner-plugin**
+- chapter focus: **Chapter 1: Obsidian Plugin Architecture**
+- system context: **Obsidian Outliner Plugin**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 1: Obsidian Plugin 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
+
+- [Obsidian Outliner](https://github.com/vslinko/obsidian-outliner)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 1: Obsidian Plugin 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?
diff --git a/tutorials/obsidian-outliner-plugin/05-keyboard-shortcuts.md b/tutorials/obsidian-outliner-plugin/05-keyboard-shortcuts.md
index 29a52dfb..80e4f730 100644
--- a/tutorials/obsidian-outliner-plugin/05-keyboard-shortcuts.md
+++ b/tutorials/obsidian-outliner-plugin/05-keyboard-shortcuts.md
@@ -82,3 +82,505 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 6: Testing and Debugging](06-testing-debugging.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- tutorial slug: **obsidian-outliner-plugin**
+- chapter focus: **Chapter 5: Keyboard Shortcuts**
+- system context: **Obsidian Outliner Plugin**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Keyboard Shortcuts`.
+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
+
+- [Obsidian Outliner](https://github.com/vslinko/obsidian-outliner)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Keyboard Shortcuts`.
+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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Keyboard Shortcuts
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/obsidian-outliner-plugin/06-testing-debugging.md b/tutorials/obsidian-outliner-plugin/06-testing-debugging.md
index 0bfa24e0..88871342 100644
--- a/tutorials/obsidian-outliner-plugin/06-testing-debugging.md
+++ b/tutorials/obsidian-outliner-plugin/06-testing-debugging.md
@@ -93,3 +93,493 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 7: Plugin Packaging](07-plugin-packaging.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- tutorial slug: **obsidian-outliner-plugin**
+- chapter focus: **Chapter 6: Testing and Debugging**
+- system context: **Obsidian Outliner Plugin**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Testing 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
+
+- [Obsidian Outliner](https://github.com/vslinko/obsidian-outliner)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Testing 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Testing and Debugging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/obsidian-outliner-plugin/07-plugin-packaging.md b/tutorials/obsidian-outliner-plugin/07-plugin-packaging.md
index 078e2f40..9078a43c 100644
--- a/tutorials/obsidian-outliner-plugin/07-plugin-packaging.md
+++ b/tutorials/obsidian-outliner-plugin/07-plugin-packaging.md
@@ -86,3 +86,505 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 8: Production Maintenance](08-production-maintenance.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- tutorial slug: **obsidian-outliner-plugin**
+- chapter focus: **Chapter 7: Plugin Packaging**
+- system context: **Obsidian Outliner Plugin**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Plugin 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
+
+- [Obsidian Outliner](https://github.com/vslinko/obsidian-outliner)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Plugin 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 7: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Plugin Packaging
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/obsidian-outliner-plugin/08-production-maintenance.md b/tutorials/obsidian-outliner-plugin/08-production-maintenance.md
index 09608e1d..cd3f8745 100644
--- a/tutorials/obsidian-outliner-plugin/08-production-maintenance.md
+++ b/tutorials/obsidian-outliner-plugin/08-production-maintenance.md
@@ -86,3 +86,505 @@ Suggested trace strategy:
 - [Previous Chapter: Chapter 7: Plugin Packaging](07-plugin-packaging.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- tutorial slug: **obsidian-outliner-plugin**
+- chapter focus: **Chapter 8: Production Maintenance**
+- system context: **Obsidian Outliner Plugin**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production Maintenance`.
+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
+
+- [Obsidian Outliner](https://github.com/vslinko/obsidian-outliner)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production Maintenance`.
+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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Maintenance
+
+- tutorial context: **Obsidian Outliner Plugin: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/obsidian-outliner-plugin/index.md b/tutorials/obsidian-outliner-plugin/index.md
index 06526c3d..b0491e74 100644
--- a/tutorials/obsidian-outliner-plugin/index.md
+++ b/tutorials/obsidian-outliner-plugin/index.md
@@ -3,6 +3,7 @@ layout: default
 title: "Obsidian Outliner Plugin"
 nav_order: 41
 has_children: true
+format_version: v2
 ---
 
 # Obsidian Outliner Plugin: Deep Dive Tutorial
@@ -13,6 +14,16 @@ has_children: true
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![TypeScript](https://img.shields.io/badge/TypeScript-Obsidian_API-blue)](https://github.com/vslinko/obsidian-outliner)
 
+## Why This Track Matters
+
+The Obsidian Outliner plugin is an ideal case study for Obsidian plugin development — it covers the full arc from API integration and CodeMirror editor extensions to tree data structures and production maintenance.
+
+This track focuses on:
+- understanding the Obsidian plugin lifecycle and API boundaries
+- implementing custom editing behaviors with CodeMirror 6
+- managing hierarchical list structures with tree manipulation algorithms
+- packaging, releasing, and maintaining a production Obsidian plugin
+
 ## What Is This Tutorial?
 
 This tutorial uses the Obsidian Outliner plugin as a case study for understanding Obsidian plugin development patterns — including editor extensions, tree data structures, keyboard shortcuts, and the Obsidian Plugin API.
@@ -25,7 +36,7 @@ This tutorial uses the Obsidian Outliner plugin as a case study for understandin
 | **Keyboard Shortcuts** | Custom hotkey handling and command registration |
 | **Performance** | Efficient algorithms for large documents |
 
-## Architecture Overview
+## Mental Model
 
 ```mermaid
 graph TB
@@ -48,7 +59,7 @@ graph TB
     KEYS --> COMMANDS
 ```
 
-## Tutorial Structure
+## Chapter Guide
 
 | Chapter | Topic | What You'll Learn |
 |---------|-------|-------------------|
@@ -97,6 +108,19 @@ Ready to begin? Start with [Chapter 1: Plugin Architecture](01-plugin-architectu
 7. [Chapter 7: Plugin Packaging](07-plugin-packaging.md)
 8. [Chapter 8: Production Maintenance](08-production-maintenance.md)
 
+## Current Snapshot (auto-updated)
+
+- repository: [vslinko/obsidian-outliner](https://github.com/vslinko/obsidian-outliner)
+- stars: about **2.5K**
+- project positioning: popular Obsidian plugin adding outliner-style editing to Obsidian notes
+
+## What You Will Learn
+
+- how the Obsidian Plugin API and CodeMirror 6 are used to extend editor behavior
+- how tree data structures model and manipulate hierarchical markdown lists
+- how keyboard shortcuts, commands, and hotkeys are registered and managed
+- how to package, version, and maintain an Obsidian plugin for long-term compatibility
+
 ## Source References
 
 - [Obsidian Outliner](https://github.com/vslinko/obsidian-outliner)
diff --git a/tutorials/openai-whisper-tutorial/01-getting-started.md b/tutorials/openai-whisper-tutorial/01-getting-started.md
index 60008a0c..0f6a2d70 100644
--- a/tutorials/openai-whisper-tutorial/01-getting-started.md
+++ b/tutorials/openai-whisper-tutorial/01-getting-started.md
@@ -104,3 +104,484 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 2: Model Architecture](02-model-architecture.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- tutorial slug: **openai-whisper-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Openai Whisper 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
+
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Cross-Tutorial Connection Map
+
+- [Whisper.cpp Tutorial](../whisper-cpp-tutorial/)
+- [OpenAI Realtime Agents Tutorial](../openai-realtime-agents-tutorial/)
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/openai-whisper-tutorial/02-model-architecture.md b/tutorials/openai-whisper-tutorial/02-model-architecture.md
index d753e5b3..e8c00781 100644
--- a/tutorials/openai-whisper-tutorial/02-model-architecture.md
+++ b/tutorials/openai-whisper-tutorial/02-model-architecture.md
@@ -100,3 +100,484 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 3: Audio Preprocessing](03-audio-preprocessing.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- tutorial slug: **openai-whisper-tutorial**
+- chapter focus: **Chapter 2: Model Architecture**
+- system context: **Openai Whisper Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Model 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
+
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Cross-Tutorial Connection Map
+
+- [Whisper.cpp Tutorial](../whisper-cpp-tutorial/)
+- [OpenAI Realtime Agents Tutorial](../openai-realtime-agents-tutorial/)
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Model 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Model Architecture
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/openai-whisper-tutorial/03-audio-preprocessing.md b/tutorials/openai-whisper-tutorial/03-audio-preprocessing.md
index 0452dcb1..18a5629d 100644
--- a/tutorials/openai-whisper-tutorial/03-audio-preprocessing.md
+++ b/tutorials/openai-whisper-tutorial/03-audio-preprocessing.md
@@ -95,3 +95,496 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 4: Transcription and Translation](04-transcription-translation.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- tutorial slug: **openai-whisper-tutorial**
+- chapter focus: **Chapter 3: Audio Preprocessing**
+- system context: **Openai Whisper Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Audio Preprocessing`.
+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
+
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Cross-Tutorial Connection Map
+
+- [Whisper.cpp Tutorial](../whisper-cpp-tutorial/)
+- [OpenAI Realtime Agents Tutorial](../openai-realtime-agents-tutorial/)
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Audio Preprocessing`.
+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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Audio Preprocessing
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/openai-whisper-tutorial/04-transcription-translation.md b/tutorials/openai-whisper-tutorial/04-transcription-translation.md
index 2a6b089a..6870cf0e 100644
--- a/tutorials/openai-whisper-tutorial/04-transcription-translation.md
+++ b/tutorials/openai-whisper-tutorial/04-transcription-translation.md
@@ -97,3 +97,484 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 5: Fine-Tuning and Adaptation](05-fine-tuning.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- tutorial slug: **openai-whisper-tutorial**
+- chapter focus: **Chapter 4: Transcription and Translation**
+- system context: **Openai Whisper Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Transcription and Translation`.
+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
+
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Cross-Tutorial Connection Map
+
+- [Whisper.cpp Tutorial](../whisper-cpp-tutorial/)
+- [OpenAI Realtime Agents Tutorial](../openai-realtime-agents-tutorial/)
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Transcription and Translation`.
+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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Transcription and Translation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/openai-whisper-tutorial/05-fine-tuning.md b/tutorials/openai-whisper-tutorial/05-fine-tuning.md
index 5011fc56..0782b387 100644
--- a/tutorials/openai-whisper-tutorial/05-fine-tuning.md
+++ b/tutorials/openai-whisper-tutorial/05-fine-tuning.md
@@ -95,3 +95,496 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 6: Advanced Features](06-advanced-features.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- tutorial slug: **openai-whisper-tutorial**
+- chapter focus: **Chapter 5: Fine-Tuning and Adaptation**
+- system context: **Openai Whisper Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Fine-Tuning and Adaptation`.
+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
+
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Cross-Tutorial Connection Map
+
+- [Whisper.cpp Tutorial](../whisper-cpp-tutorial/)
+- [OpenAI Realtime Agents Tutorial](../openai-realtime-agents-tutorial/)
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Fine-Tuning and Adaptation`.
+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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Fine-Tuning and Adaptation
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/openai-whisper-tutorial/06-advanced-features.md b/tutorials/openai-whisper-tutorial/06-advanced-features.md
index 5f53013a..849fb9ec 100644
--- a/tutorials/openai-whisper-tutorial/06-advanced-features.md
+++ b/tutorials/openai-whisper-tutorial/06-advanced-features.md
@@ -97,3 +97,484 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 7: Performance Optimization](07-performance-optimization.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- tutorial slug: **openai-whisper-tutorial**
+- chapter focus: **Chapter 6: Advanced Features**
+- system context: **Openai Whisper Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Advanced Features`.
+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
+
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Cross-Tutorial Connection Map
+
+- [Whisper.cpp Tutorial](../whisper-cpp-tutorial/)
+- [OpenAI Realtime Agents Tutorial](../openai-realtime-agents-tutorial/)
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Advanced Features`.
+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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Advanced Features
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/openai-whisper-tutorial/07-performance-optimization.md b/tutorials/openai-whisper-tutorial/07-performance-optimization.md
index 037acb95..ec0cb74b 100644
--- a/tutorials/openai-whisper-tutorial/07-performance-optimization.md
+++ b/tutorials/openai-whisper-tutorial/07-performance-optimization.md
@@ -88,3 +88,496 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- tutorial slug: **openai-whisper-tutorial**
+- chapter focus: **Chapter 7: Performance Optimization**
+- system context: **Openai Whisper Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Performance Optimization`.
+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
+
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Cross-Tutorial Connection Map
+
+- [Whisper.cpp Tutorial](../whisper-cpp-tutorial/)
+- [OpenAI Realtime Agents Tutorial](../openai-realtime-agents-tutorial/)
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Performance Optimization`.
+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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Performance Optimization
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/openai-whisper-tutorial/08-production-deployment.md b/tutorials/openai-whisper-tutorial/08-production-deployment.md
index d1801efb..68cd230e 100644
--- a/tutorials/openai-whisper-tutorial/08-production-deployment.md
+++ b/tutorials/openai-whisper-tutorial/08-production-deployment.md
@@ -96,3 +96,496 @@ Suggested trace strategy:
 - [Previous Chapter: Chapter 7: Performance Optimization](07-performance-optimization.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- tutorial slug: **openai-whisper-tutorial**
+- chapter focus: **Chapter 8: Production Deployment**
+- system context: **Openai Whisper Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production 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
+
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Cross-Tutorial Connection Map
+
+- [Whisper.cpp Tutorial](../whisper-cpp-tutorial/)
+- [OpenAI Realtime Agents Tutorial](../openai-realtime-agents-tutorial/)
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [Chapter 1: Getting Started](01-getting-started.md)
+- [openai/whisper repository](https://github.com/openai/whisper)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production 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 8: Production Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **OpenAI Whisper Tutorial: Speech Recognition and Translation**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/openai-whisper-tutorial/index.md b/tutorials/openai-whisper-tutorial/index.md
index e6c6585f..c7ee6608 100644
--- a/tutorials/openai-whisper-tutorial/index.md
+++ b/tutorials/openai-whisper-tutorial/index.md
@@ -3,6 +3,7 @@ layout: default
 title: "OpenAI Whisper Tutorial"
 nav_order: 90
 has_children: true
+format_version: v2
 ---
 
 # OpenAI Whisper Tutorial: Speech Recognition and Translation
@@ -13,6 +14,16 @@ has_children: true
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Paper](https://img.shields.io/badge/Paper-arXiv-blue)](https://arxiv.org/abs/2212.04356)
 
+## Why This Track Matters
+
+Whisper is the most widely deployed open-source speech recognition model, and understanding how to use it effectively — from audio preprocessing to production deployment — is essential for building robust transcription pipelines.
+
+This track focuses on:
+- transcribing and translating audio with Whisper's multilingual model family
+- preprocessing audio for optimal recognition accuracy
+- optimizing Whisper for throughput with batching and hardware acceleration
+- deploying Whisper as a production service with observability and retry strategies
+
 ## What Whisper is
 
 Whisper is an open-source speech model family trained for multilingual transcription, language identification, and speech-to-English translation.
@@ -29,7 +40,7 @@ The official repository provides:
 - The `turbo` model is optimized for fast transcription but is not recommended for translation tasks.
 - Accuracy and speed vary significantly by language, audio quality, and hardware.
 
-## Tutorial Structure
+## Chapter Guide
 
 | Chapter | Topic | What You Will Learn |
 |:--------|:------|:--------------------|
@@ -84,11 +95,24 @@ Ready to begin? Start with [Chapter 1: Getting Started](01-getting-started.md).
 7. [Chapter 7: Performance Optimization](07-performance-optimization.md)
 8. [Chapter 8: Production Deployment](08-production-deployment.md)
 
+## Current Snapshot (auto-updated)
+
+- repository: [openai/whisper](https://github.com/openai/whisper)
+- stars: about **76K**
+- project positioning: open-source multilingual speech recognition model from OpenAI
+
+## What You Will Learn
+
+- how Whisper's encoder-decoder architecture and multitask token system work
+- how to preprocess audio with resampling, normalization, and segmentation
+- how to optimize Whisper performance with model sizing, batching, and quantization
+- how to deploy Whisper as a production service with proper observability and governance
+
 ## Source References
 
 - [openai/whisper repository](https://github.com/openai/whisper)
 
-## Concept Flow
+## Mental Model
 
 ```mermaid
 flowchart TD
diff --git a/tutorials/teable-database-platform/04-api-development.md b/tutorials/teable-database-platform/04-api-development.md
index bccb6973..c754ec72 100644
--- a/tutorials/teable-database-platform/04-api-development.md
+++ b/tutorials/teable-database-platform/04-api-development.md
@@ -86,3 +86,505 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 5: Realtime Collaboration](05-realtime-collaboration.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Teable: Deep Dive Tutorial**
+- tutorial slug: **teable-database-platform**
+- chapter focus: **Chapter 4: API Development**
+- system context: **Teable Database Platform**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: API Development`.
+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
+
+- [Teable](https://github.com/teableio/teable)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: API Development`.
+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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Development
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/teable-database-platform/05-realtime-collaboration.md b/tutorials/teable-database-platform/05-realtime-collaboration.md
index 948e300c..982b7df4 100644
--- a/tutorials/teable-database-platform/05-realtime-collaboration.md
+++ b/tutorials/teable-database-platform/05-realtime-collaboration.md
@@ -86,3 +86,505 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 6: Query System](06-query-system.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Teable: Deep Dive Tutorial**
+- tutorial slug: **teable-database-platform**
+- chapter focus: **Chapter 5: Realtime Collaboration**
+- system context: **Teable Database Platform**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Realtime Collaboration`.
+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
+
+- [Teable](https://github.com/teableio/teable)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Realtime Collaboration`.
+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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Realtime Collaboration
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/teable-database-platform/06-query-system.md b/tutorials/teable-database-platform/06-query-system.md
index 32ac09ef..697ea888 100644
--- a/tutorials/teable-database-platform/06-query-system.md
+++ b/tutorials/teable-database-platform/06-query-system.md
@@ -85,3 +85,505 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 7: Frontend Architecture](07-frontend-architecture.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Teable: Deep Dive Tutorial**
+- tutorial slug: **teable-database-platform**
+- chapter focus: **Chapter 6: Query System**
+- system context: **Teable Database Platform**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: Query System`.
+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
+
+- [Teable](https://github.com/teableio/teable)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: Query System`.
+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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Query System
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/teable-database-platform/07-frontend-architecture.md b/tutorials/teable-database-platform/07-frontend-architecture.md
index e44a1e60..19178d47 100644
--- a/tutorials/teable-database-platform/07-frontend-architecture.md
+++ b/tutorials/teable-database-platform/07-frontend-architecture.md
@@ -86,3 +86,505 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 8: Production Deployment](08-production-deployment.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Teable: Deep Dive Tutorial**
+- tutorial slug: **teable-database-platform**
+- chapter focus: **Chapter 7: Frontend Architecture**
+- system context: **Teable Database Platform**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Frontend 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
+
+- [Teable](https://github.com/teableio/teable)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Frontend 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Frontend Architecture
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/teable-database-platform/08-production-deployment.md b/tutorials/teable-database-platform/08-production-deployment.md
index 65a74743..63b2e269 100644
--- a/tutorials/teable-database-platform/08-production-deployment.md
+++ b/tutorials/teable-database-platform/08-production-deployment.md
@@ -87,3 +87,505 @@ Suggested trace strategy:
 - [Previous Chapter: Chapter 7: Frontend Architecture](07-frontend-architecture.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **Teable: Deep Dive Tutorial**
+- tutorial slug: **teable-database-platform**
+- chapter focus: **Chapter 8: Production Deployment**
+- system context: **Teable Database Platform**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Production 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
+
+- [Teable](https://github.com/teableio/teable)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- Related tutorials are listed in this tutorial index.
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Production 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 8: Production Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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 Deployment
+
+- tutorial context: **Teable: Deep Dive Tutorial**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/teable-database-platform/index.md b/tutorials/teable-database-platform/index.md
index 8192d98f..5a468c58 100644
--- a/tutorials/teable-database-platform/index.md
+++ b/tutorials/teable-database-platform/index.md
@@ -3,6 +3,7 @@ layout: default
 title: "Teable Database Platform"
 nav_order: 42
 has_children: true
+format_version: v2
 ---
 
 # Teable: Deep Dive Tutorial
@@ -13,6 +14,16 @@ has_children: true
 [![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
 [![TypeScript](https://img.shields.io/badge/TypeScript-Next.js-blue)](https://github.com/teableio/teable)
 
+## Why This Track Matters
+
+Teable combines the power of PostgreSQL with a collaborative spreadsheet interface, offering teams a scalable no-code database that doesn't sacrifice data integrity or query performance for usability.
+
+This track focuses on:
+- building on PostgreSQL with Teable's schema management and query system
+- implementing real-time collaborative editing with WebSocket consistency
+- generating and consuming REST and GraphQL APIs from Teable tables
+- deploying and scaling Teable with Docker for production workloads
+
 ## What Is Teable?
 
 Teable is a high-performance, multi-dimensional database platform that combines the power of PostgreSQL with a spreadsheet-like UI. It supports real-time collaboration, complex data relationships, and advanced querying — offering a scalable alternative to Airtable built on proven database technology.
@@ -26,7 +37,7 @@ Teable is a high-performance, multi-dimensional database platform that combines
 | **REST & GraphQL** | Auto-generated APIs with schema validation |
 | **Self-Hosted** | Docker deployment with horizontal scaling |
 
-## Architecture Overview
+## Mental Model
 
 ```mermaid
 graph TB
@@ -54,7 +65,7 @@ graph TB
     Backend --> Data
 ```
 
-## Tutorial Structure
+## Chapter Guide
 
 | Chapter | Topic | What You'll Learn |
 |---------|-------|-------------------|
@@ -105,6 +116,19 @@ Ready to begin? Start with [Chapter 1: System Overview](01-system-overview.md).
 7. [Chapter 7: Frontend Architecture](07-frontend-architecture.md)
 8. [Chapter 8: Production Deployment](08-production-deployment.md)
 
+## Current Snapshot (auto-updated)
+
+- repository: [teableio/teable](https://github.com/teableio/teable)
+- stars: about **15K**
+- project positioning: high-performance PostgreSQL-native no-code database with real-time collaboration
+
+## What You Will Learn
+
+- how Teable uses PostgreSQL as its native storage layer with schema management and indexing
+- how WebSocket-based real-time collaboration handles multi-user consistency
+- how the query system translates view-driven filters into optimized PostgreSQL queries
+- how to deploy and scale Teable with Docker Compose for production environments
+
 ## Source References
 
 - [Teable](https://github.com/teableio/teable)
diff --git a/tutorials/tiktoken-tutorial/01-getting-started.md b/tutorials/tiktoken-tutorial/01-getting-started.md
index 69bc210d..11b89eb4 100644
--- a/tutorials/tiktoken-tutorial/01-getting-started.md
+++ b/tutorials/tiktoken-tutorial/01-getting-started.md
@@ -108,3 +108,483 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 2: Tokenization Mechanics](02-tokenization-mechanics.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- tutorial slug: **tiktoken-tutorial**
+- chapter focus: **Chapter 1: Getting Started**
+- system context: **Tiktoken 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
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+
+### 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/tiktoken-tutorial/02-tokenization-mechanics.md b/tutorials/tiktoken-tutorial/02-tokenization-mechanics.md
index c09f1af9..cd7c94ea 100644
--- a/tutorials/tiktoken-tutorial/02-tokenization-mechanics.md
+++ b/tutorials/tiktoken-tutorial/02-tokenization-mechanics.md
@@ -99,3 +99,483 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 3: Practical Applications](03-practical-applications.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- tutorial slug: **tiktoken-tutorial**
+- chapter focus: **Chapter 2: Tokenization Mechanics**
+- system context: **Tiktoken Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 2: Tokenization Mechanics`.
+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
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 2: Tokenization Mechanics`.
+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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Tokenization Mechanics
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/tiktoken-tutorial/03-practical-applications.md b/tutorials/tiktoken-tutorial/03-practical-applications.md
index 0987c3c3..f50b0b68 100644
--- a/tutorials/tiktoken-tutorial/03-practical-applications.md
+++ b/tutorials/tiktoken-tutorial/03-practical-applications.md
@@ -103,3 +103,483 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 4: Educational Module](04-educational-module.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- tutorial slug: **tiktoken-tutorial**
+- chapter focus: **Chapter 3: Practical Applications**
+- system context: **Tiktoken Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 3: Practical Applications`.
+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
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 3: Practical Applications`.
+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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Practical Applications
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/tiktoken-tutorial/04-educational-module.md b/tutorials/tiktoken-tutorial/04-educational-module.md
index 4cdfb0e1..2ecc10b4 100644
--- a/tutorials/tiktoken-tutorial/04-educational-module.md
+++ b/tutorials/tiktoken-tutorial/04-educational-module.md
@@ -94,3 +94,495 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 5: Optimization Strategies](05-optimization-strategies.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- tutorial slug: **tiktoken-tutorial**
+- chapter focus: **Chapter 4: Educational Module**
+- system context: **Tiktoken Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 4: Educational Module`.
+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
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 4: Educational Module`.
+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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Educational Module
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/tiktoken-tutorial/05-optimization-strategies.md b/tutorials/tiktoken-tutorial/05-optimization-strategies.md
index ef6a7e39..d9812176 100644
--- a/tutorials/tiktoken-tutorial/05-optimization-strategies.md
+++ b/tutorials/tiktoken-tutorial/05-optimization-strategies.md
@@ -109,3 +109,483 @@ Suggested trace strategy:
 - [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)
+
+## Depth Expansion Playbook
+
+<!-- depth-expansion-v2 -->
+
+This chapter is expanded to v1-style depth for production-grade learning and implementation quality.
+
+### Strategic Context
+
+- tutorial: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- tutorial slug: **tiktoken-tutorial**
+- chapter focus: **Chapter 5: Optimization Strategies**
+- system context: **Tiktoken Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 5: Optimization 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
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 5: Optimization 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 5: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Optimization Strategies
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/tiktoken-tutorial/06-chatml-and-tool-calls.md b/tutorials/tiktoken-tutorial/06-chatml-and-tool-calls.md
index e08913fc..98bfc2b6 100644
--- a/tutorials/tiktoken-tutorial/06-chatml-and-tool-calls.md
+++ b/tutorials/tiktoken-tutorial/06-chatml-and-tool-calls.md
@@ -101,3 +101,483 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 7: Multilingual Tokenization](07-multilingual-tokenization.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- tutorial slug: **tiktoken-tutorial**
+- chapter focus: **Chapter 6: ChatML and Tool Call Accounting**
+- system context: **Tiktoken Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 6: ChatML and Tool Call Accounting`.
+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
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 6: ChatML and Tool Call Accounting`.
+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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: ChatML and Tool Call Accounting
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/tiktoken-tutorial/07-multilingual-tokenization.md b/tutorials/tiktoken-tutorial/07-multilingual-tokenization.md
index a678570c..a72dfb35 100644
--- a/tutorials/tiktoken-tutorial/07-multilingual-tokenization.md
+++ b/tutorials/tiktoken-tutorial/07-multilingual-tokenization.md
@@ -97,3 +97,495 @@ Suggested trace strategy:
 - [Next Chapter: Chapter 8: Cost Governance](08-cost-governance.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- tutorial slug: **tiktoken-tutorial**
+- chapter focus: **Chapter 7: Multilingual Tokenization**
+- system context: **Tiktoken Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 7: Multilingual Tokenization`.
+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
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 7: Multilingual Tokenization`.
+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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Multilingual Tokenization
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/tiktoken-tutorial/08-cost-governance.md b/tutorials/tiktoken-tutorial/08-cost-governance.md
index f9dcc397..2d584c83 100644
--- a/tutorials/tiktoken-tutorial/08-cost-governance.md
+++ b/tutorials/tiktoken-tutorial/08-cost-governance.md
@@ -98,3 +98,483 @@ Suggested trace strategy:
 - [Previous Chapter: Chapter 7: Multilingual Tokenization](07-multilingual-tokenization.md)
 - [Main Catalog](../../README.md#-tutorial-catalog)
 - [A-Z Tutorial Directory](../../discoverability/tutorial-directory.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: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- tutorial slug: **tiktoken-tutorial**
+- chapter focus: **Chapter 8: Cost Governance**
+- system context: **Tiktoken Tutorial**
+- objective: move from surface-level usage to repeatable engineering operation
+
+### Architecture Decomposition
+
+1. Define the runtime boundary for `Chapter 8: Cost 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
+
+- [tiktoken repository](https://github.com/openai/tiktoken)
+- [AI Codebase Knowledge Builder](https://github.com/The-Pocket/Tutorial-Codebase-Knowledge)
+
+### Cross-Tutorial Connection Map
+
+- [OpenAI Python SDK Tutorial](../openai-python-sdk-tutorial/)
+- [LangChain Tutorial](../langchain-tutorial/)
+- [LlamaIndex Tutorial](../llamaindex-tutorial/)
+
+### Advanced Practice Exercises
+
+1. Build a minimal end-to-end implementation for `Chapter 8: Cost 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: schema updates introduce incompatible payloads
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: pin schema versions and add compatibility shims
+- verification target: throughput remains stable under target concurrency
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: environment parity drifts between staging and production
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: restore environment parity via immutable config promotion
+- verification target: retry volume stays bounded without feedback loops
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: access policy changes reduce successful execution rates
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: re-scope credentials and rotate leaked or stale keys
+- verification target: data integrity checks pass across write/read cycles
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: background jobs accumulate and exceed processing windows
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: activate degradation mode to preserve core user paths
+- verification target: audit logs capture all control-plane mutations
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: incoming request volume spikes after release
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: introduce adaptive concurrency limits and queue bounds
+- verification target: latency p95 and p99 stay within defined SLO windows
+- rollback trigger: pre-defined quality gate fails for 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: Cost Governance
+
+- tutorial context: **tiktoken Tutorial: OpenAI Token Encoding & Optimization**
+- trigger condition: tool dependency latency increases under concurrency
+- initial hypothesis: identify the smallest reproducible failure boundary
+- immediate action: protect user-facing stability before optimization work
+- engineering control: enable staged retries with jitter and circuit breaker fallback
+- verification target: error budget burn rate remains below escalation threshold
+- rollback trigger: pre-defined quality gate fails for two consecutive checks
+- communication step: publish incident status with owner and ETA
+- learning capture: add postmortem and convert findings into automated tests
diff --git a/tutorials/tiktoken-tutorial/index.md b/tutorials/tiktoken-tutorial/index.md
index 2bcf14cc..22d98218 100644
--- a/tutorials/tiktoken-tutorial/index.md
+++ b/tutorials/tiktoken-tutorial/index.md
@@ -3,6 +3,7 @@ layout: default
 title: "tiktoken Tutorial"
 nav_order: 94
 has_children: true
+format_version: v2
 ---
 
 # tiktoken Tutorial: OpenAI Token Encoding & Optimization
@@ -13,6 +14,16 @@ has_children: true
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Python](https://img.shields.io/badge/Python-Rust-blue)](https://github.com/openai/tiktoken)
 
+## Why This Track Matters
+
+Accurate token counting is the foundation of cost control, context management, and reliable API usage with GPT models — tiktoken provides the exact same tokenization OpenAI uses, making it essential for any production OpenAI integration.
+
+This track focuses on:
+- counting tokens accurately before making API calls to control costs
+- understanding BPE tokenization and how encoding choices affect model behavior
+- optimizing prompts and chunking strategies for context window management
+- building token-aware applications for RAG, chat, and API cost governance
+
 ## 🎯 What is tiktoken?
 
 **tiktoken** is a fast Byte Pair Encoding (BPE) tokenizer library created by OpenAI for use with their models. It's 3-6x faster than comparable tokenizers and provides accurate token counting for GPT models, enabling precise cost estimation and context management.
@@ -28,7 +39,7 @@ has_children: true
 | **Reversible** | Lossless encoding/decoding of any text |
 | **Efficient** | ~4 bytes per token on average, excellent compression |
 
-## Architecture Overview
+## Mental Model
 
 ```mermaid
 graph LR
@@ -66,7 +77,7 @@ graph LR
     class TOKENS,COUNT,DECODED output
 ```
 
-## Tutorial Structure
+## Chapter Guide
 
 | Chapter | Topic | What You'll Learn |
 |:--------|:------|:------------------|
@@ -89,7 +100,7 @@ graph LR
 | **Supported Encodings** | cl100k_base, p50k_base, r50k_base, p50k_edit, gpt2 |
 | **Installation** | pip (pre-compiled wheels) |
 
-## What You'll Learn
+## What You Will Learn
 
 By the end of this tutorial, you'll be able to:
 
@@ -187,6 +198,12 @@ Ready to begin? Start with [Chapter 1: Getting Started](01-getting-started.md).
 7. [Chapter 7: Multilingual Tokenization](07-multilingual-tokenization.md)
 8. [Chapter 8: Cost Governance](08-cost-governance.md)
 
+## Current Snapshot (auto-updated)
+
+- repository: [openai/tiktoken](https://github.com/openai/tiktoken)
+- stars: about **12K**
+- project positioning: OpenAI's official fast BPE tokenizer library used by GPT models
+
 ## Source References
 
 - [tiktoken repository](https://github.com/openai/tiktoken)